Computational features of tumor-infiltrating lymphocyte (til) architecture

ABSTRACT

Various embodiments of the present disclosure are directed towards a method for generating a risk group classification for an African American (AA) patient. The method includes extracting a first plurality of architectural features from a digitized H&amp;E slide image of the AA patient. A risk score for the AA patient is generated based on the first plurality of architectural features, where the risk score is prognostic of overall survival (OS) of the AA patient. The risk group classification is generated for the AA patient, where generating the risk group classification includes classifying the AA patient into either a high risk group or a low risk group based on the risk score, where the high risk group indicates the AA patient will die before a threshold date and the low risk group indicates the AA patient will die after or on the threshold date.

REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.63/212,231 filed on Jun. 18, 2021, the contents of which are herebyincorporated by reference in their entirety.

FEDERAL FUNDING NOTICE

This invention was made with government support under W81XWH-19-1-0668,W81XWH-15-1-0558, W81XWH-20-1-0851, W81XWH-18-1-0440, W81XWH-20-1-0595,and W81XWH-18-1-0404 awarded by the Department of Defense, and CA199374,CA249992, CA202752, CA208236, CA216579, CA220581, CA239055, CA248226,CA254566, HL151277, EB028736, RR012463, and TR000254 awarded by theNational Institutes of Health. The government has certain rights in theinvention.

BACKGROUND

The uterus is a hollow organ, normally about a size and shape of amedium-sized pear. The uterus is where a fetus grows and develops when awoman is pregnant. The uterus comprises an outer layer known as themyometrium and an inner layer known as the endometrium. Endometrialcancer starts in cells of the inner layer (e.g., the endometrium) of theuterus.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate various example operations,apparatus, methods, and other example embodiments of various aspectsdiscussed herein. It will be appreciated that the illustrated elementboundaries (e.g., boxes, groups of boxes, or other shapes) in thefigures represent one example of the boundaries. One of ordinary skillin the art will appreciate that, in some examples, one element can bedesigned as multiple elements or that multiple elements can be designedas one element. In some examples, an element shown as an internalcomponent of another element may be implemented as an external componentand vice versa. Furthermore, elements may not be drawn to scale.

FIG. 1 illustrates some embodiments of a method for generating aprediction for overall survival (OS) of an African American (AA) patientwith endometrial cancer (EC).

FIG. 2 illustrates some embodiments of a method for generating aprediction to a response to a treatment plan for EC for an AA patientwith EC.

FIG. 3 illustrates some embodiments of a method for generating both aprediction for OS of an AA patient with EC and a prediction to aresponse to a treatment plan for EC for the AA patient with EC.

FIG. 4 illustrates some embodiments of a method for classifying an AApatient's EC as being an aggressive subtype of EC or a non-aggressivesubtype of EC.

FIG. 5 illustrates a method of some more detailed embodiments of theseventh operation of the method of FIG. 1 .

FIG. 6 illustrates a method of some other embodiments of the method ofFIG. 2 .

FIG. 7 illustrates a method of some embodiments of the first operationof the method of FIG. 6 .

FIG. 8 illustrates some embodiments of a method for classifying whetherthe EC of an AA POI is either an aggressive subtype of EC or anon-aggressive subtype of EC.

FIG. 9 illustrates a method of some other embodiments of the method ofFIG. 7 .

FIG. 10 illustrates a graphical representation of the criteria of adataset for Example Use Case 1.

FIG. 11 illustrates a graphical representation of some embodiments forquantifying TIL arrangements for Example Use Case 1.

FIG. 12 illustrates various plots associated with survival analysisresults for a population-agnostic model (M_(AA+CA)) of the Example UseCase 1.

FIG. 13 illustrates digitized H&E slide images of a long-term patientand digitized H&E slide images of a short-term patient of the ExampleUse Case 1.

FIG. 14 illustrates digitized H&E slide images of a long-term survivingAA patient and digitized H&E slide images of a short-term surviving AApatient for Example Use Case 1.

FIG. 15 illustrates various plots associated with survival analysisresults for population-specific models ((M_(AA)) and (M_(CA))) of theExample Use Case 1.

FIG. 16 illustrates digitized H&E slide images of a long-term survivingCA patient and digitized H&E slide images of a short-term surviving CApatient for Example Use Case 1.

FIG. 17 illustrates various plots associated with survival analysisresults for the population-specific models ((M_(AA)) and (M_(CA))) ofthe Example Use Case 1.

FIG. 18 illustrates a graphical representation of an overview of theresults of Example Use Case 1.

FIG. 19 illustrates some embodiments of an apparatus that can facilitatethe methods described herein.

FIG. 20 illustrates some other embodiments of the apparatus of FIG. 19 .

FIG. 21 illustrates some embodiments of a computer in which methodsdescribed herein can operate and in which example methods, apparatus,circuits, operations, or logics may be implemented

DETAILED DESCRIPTION

The description herein is made with reference to the drawings, whereinlike reference numerals are generally utilized to refer to like elementsthroughout, and wherein the various structures are not necessarily drawnto scale. In the following description, for purposes of explanation,numerous specific details are set forth in order to facilitateunderstanding. It may be evident, however, to one of ordinary skill inthe art, that one or more aspects described herein may be practiced witha lesser degree of these specific details. In other instances, knownstructures and devices are shown in block diagram form to facilitateunderstanding.

Endometrial cancer (EC) is typically caught in early-stages and thus istreatable (e.g., with at least 85% 5-year overall survival (OS)) bysurgery, chemotherapy, and/or radiotherapy. However, a fraction of ECcases are aggressive neoplasms such as high-grade or deeply invasivelesions (e.g., aggressive EC) and thus exhibit poor prognosis. It hasbeen appreciated that EC may disproportionally affect differentpopulation groups (e.g., difference races, ethnicities, etc.). Forexample, African American (AA) women are disproportionately affected byhigh-grade EC than Caucasian American (CA) women, and thus AA womenhaving EC may have a mortality rate that is approximately 80% higherthan that of CA women having EC.

Various embodiments of the present disclosure relate to a method (andrelated apparatus) for determining prognostic features that areindicative of EC for different population groups (e.g., differenceraces, ethnicities, etc.). The method utilizes computational methods toidentify prognostic features of architectural features oftumor-infiltrating lymphocytes (ArcTIL) from hematoxylin and eosinstained slides (H&E slides) of different population groups having EC. Insome embodiments, the method may identify prognostic features of ArcTILthat are differentially prognostic of EC between population groupscomprising AA and CA women.

Further, various embodiments of the present disclosure relate to amethod (and related apparatus) that utilizes the prognostic featuresthat are indicative of EC for AA women to prognosticate overall survival(OS) of AA women (and/or prognosticate a response to treatment (e.g.,chemotherapy, radiotherapy, etc.) for the EC of the AA women). Forexample, the method may comprise accessing a digitized H&E slide imageof an African American (AA) patient (e.g., AA woman), where thedigitized H&E slide of the AA patient demonstrates tissue from a uterusof the AA patient and at least a portion of a gynecologic tumor. A tumorregion is defined in the digitized H&E slide image of the AA patient,where the tumor region comprises at least a portion of the gynecologictumor, and where the tumor region comprises a plurality of individualcells. The individual cells of the plurality of individual cells arethen classified into cell types, where the cell types comprisetumor-infiltrating lymphocytes (TILs) and non-lymphocyte cells. The TILsare then classified as stromal TILs or epithelial TILs. A cluster ofstromal TILs is generated (e.g., based on proximity of a subset ofstromal TILs). A plurality of architectural features are extracted fromthe digitized H&E slide image of the AA patient. A risk score for the AApatient is generated based on the plurality of architectural features,where the risk score is prognostic of overall survival (OS) of the AApatient. A risk group classification is generated for the AA patientbased on the risk score of the AA patient, where the risk groupclassification classifies the AA patient into either a high risk group(e.g., indicates the AA patient will die before a threshold date) or alow risk group (e.g., indicates the AA patient will die after or on thethreshold date). By utilizing a risk score for an AA patient, which isprognostic of overall survival (OS) of the AA patient, and classifyingthe patient into a high risk group or a low risk group based on the riskscore for the AA patient, treatment of the AA patient's EC may beaccurately guided to achieve better treatment results (e.g., to utilizea more aggressive treatment(s) when the AA is classified into the highrisk group and utilizing less aggressive treatment(s) when the AA isclassified into the low risk group).

Some portions of the detailed descriptions that follow are presented interms of algorithms and symbolic representations of operations on databits within a memory. These algorithmic descriptions and representationsare used by those skilled in the art to convey the substance of theirwork to others. An algorithm, here and generally, is conceived to be asequence of operations that produce a result. The operations may includephysical manipulations of physical quantities. Usually, though notnecessarily, the physical quantities take the form of electrical ormagnetic signals capable of being stored, transferred, combined,compared, and otherwise manipulated in a logic or circuit, and so on.The physical manipulations create a concrete, tangible, useful,real-world result.

It has proven convenient at times, principally for reasons of commonusage, to refer to these signals as bits, values, elements, symbols,characters, terms, numbers, and so on. It should be borne in mind,however, that these and similar terms are to be associated with theappropriate physical quantities and are merely convenient labels appliedto these quantities. Unless specifically stated otherwise, it isappreciated that throughout the description, terms including processing,computing, calculating, determining, and so on, refer to actions andprocesses of a computer system, logic, circuit, processor, or similarelectronic device that manipulates and transforms data represented asphysical (electronic) quantities.

A processor(s) may include any combination of general-purpose processorsand dedicated processors (e.g., graphics processors, applicationprocessors, etc.). The processors may be coupled with or may includememory or storage and may be configured to execute instructions storedin the memory or storage to enable various apparatus, applications, oroperating systems to perform the operations or methods described herein.The memory or storage devices may include main memory, disk storage, orany suitable combination thereof. The memory or storage devices mayinclude, but are not limited to any type of volatile or non-volatilememory such as dynamic random access memory (DRAM), static random-accessmemory (SRAM), erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM), Flashmemory, or solid-state storage.

Example methods and operations may be better appreciated with referenceto flow diagrams. While for purposes of simplicity of explanation, theillustrated methodologies are shown and described as a series of blocks,it is to be appreciated that the methodologies are not limited by theorder of the blocks, as some blocks can occur in different orders and/orconcurrently with other blocks from that shown and described. Moreover,less than all the illustrated blocks may be required to implement anexample methodology. Blocks may be combined or separated into multiplecomponents. Furthermore, additional and/or alternative methodologies canemploy additional, not illustrated blocks.

FIG. 1 illustrates some embodiments of a method 100 for generating aprediction for overall survival (OS) of an African American (AA) patientwith endometrial cancer (EC). In some embodiments, the AA patient isreferred to as a AA patient of interest (POI) (e.g., due to the method100 generating a prediction of OS for the AA patient).

The method 100 comprises a first operation 102. At the first operation102, a digitized hematoxylin and eosin stained slide (H&E slide) of anAA patient with EC is accessed. The digitized H&E slide image of the AApatient demonstrates one or more indicators of EC. The digitized H&Eslide of the AA patient demonstrates tissue from the uterus of the AApatient and at least a portion of a gynecologic tumor. The portion ofthe gynecologic tumor demonstrated in the digitized H&E slide of the AApatient may be an indicator of EC.

In some embodiments, the gynecologic tumor is disposed in theendometrium of the uterus of the AA patient (e.g., demonstrated in thedigitized H&E slide of the AA patient in the endometrium of the uterusof the AA patient) and/or the gynecologic tumor is a tumor that begin inthe cells of the endometrium of the uterus of the AA patient (e.g., thegynecologic tumor is in a different location of the uterus but began inor metastasized from the endometrium of the uterus of the AA patient).In some embodiments, the digitized H&E slide of the AA patient is adigitized H&E slide of the AA patient that was taken before the start ofa treatment plan for EC (e.g., chemotherapy, radiotherapy, hormonetherapy, etc.).

The digitized H&E slide of the AA patient may be stored in memory,either locally or remotely. The digitized H&E slide of the AA patientmay be obtained by an imaging device (e.g., a digital microscope). Forexample, in some embodiments, the digitized H&E slide of the AA patientmay be digitized by scanning the H&E slide (e.g., the physical H&Eslide) via an imaging system (e.g., the digital microscope). Thedigitized H&E slide may be captured and/or stored in a whole slide imagefile format. The digitized H&E slide of the AA patient may be obtainedconcurrently with the method 100 (e.g., via the imaging deviceimplementing method 100) or prior to the method 100 (e.g., at a timethat is before a time in which the method 100 is implemented). Accessingthe digitized H&E slide of the AA patient includes acquiring electronicdata, reading from a computer file, receiving a computer file, readingfrom a computer memory, or other computerized activity not practicallyperformed in the human mind.

The method 100 comprises a second operation 104. At the second operation104, a tumor region is defined in the digitized H&E slide of the AApatient. The tumor region comprises the portion of the gynecologictumor. In some embodiments, defining the tumor region comprises definingan outer boundary of the portion of the gynecologic tumor. In someembodiments, the tumor region comprises the portion of the gynecologictumor and a surrounding portion of tissue (e.g., a healthy portion ofthe endometrium of the uterus of the AA patient). The surroundingportion of tissue may extend around (completely or partially) the outerboundary of the portion of the gynecologic tumor. The surroundingportion of tissue may extend from (e.g., radially) the tumor region apredefined distance (e.g., 1 millimeter (mm), 2 mm, 3 mm, etc.).

Further, the tumor region comprises a plurality of individual cells. Inother words, the portion of the gynecologic tumor (and the surroundingportion of tissue) are made up of the plurality of individual cells. Theplurality of individual cells are demonstrated in the tumor region ofthe digitized H&E slide of the AA patient. The plurality of individualcells each have a specific cell type. For example, a first individualcell of the plurality of individual cells may be a tumor-infiltratinglymphocyte (TIL), a second individual cell of the plurality ofindividual cells may be a non-lymphocyte cells, a third individual cellof the plurality of individual cells may be a cancer cell, and so forth.Defining the tumor region in the digitized H&E slide of the AA patientincludes acquiring electronic data, reading from a computer file,receiving a computer file, reading from a computer memory, or othercomputerized activity not practically performed in the human mind.

The method 100 comprises a third operation 106. At the third operation106, boundaries for the plurality of individual cells are defined. Insome embodiments, an outer boundary is defined for each of the pluralityof individual cells. For example, an outer boundary for the firstindividual cell is defined, an outer boundary for the second individualcell is defined, and so forth. Defining the boundaries for the pluralityof individual cells includes acquiring electronic data, reading from acomputer file, receiving a computer file, reading from a computermemory, or other computerized activity not practically performed in thehuman mind.

The method 100 comprises a fourth operation 108. At the fourth operation108, the plurality of individual cells are classified into cell types.In some embodiments, each of the plurality of individual cells isclassified into its distinct cell type. The cell types comprisetumor-infiltrating lymphocytes (TILs), non-lymphocyte cells, cancercells. For example, the first individual cell is classified as a TIL,the second individual cell is classified as a non-lymphocyte cells, thethird individual cell is classified as a cancer cell, and so forth. Theplurality of individual cells are classified into their respective celltypes based on the physical structures of the plurality of individualcells demonstrated in the digitized H&E slide of the AA patient. It willbe appreciated that the cell types may comprise other types of humancells. Classifying the plurality of individual cells are classified intocell types includes acquiring electronic data, reading from a computerfile, receiving a computer file, reading from a computer memory, orother computerized activity not practically performed in the human mind

The method 100 comprises a fifth operation 110. At the fifth operation110, the TILs are classified as stromal TILs or epithelial TILs. Forexample, the first individual cell, which has been classified as a TIL,is one individual cell of a collection of individual cells of theplurality of individual cells. Each of the individual cells of thecollection of individual cells have also been classified as TILs. Theindividual cells of the collection of individual cells are classified asstromal TILs or epithelial TILs. In some embodiments, the individualcells of the collection of individual cells may classified into othertypes of TILs. For example, in some embodiments, the TILs are classifiedas stromal TILs, epithelial TILs, and/or intratumoral TILs. Morespecifically, in some embodiments, the TILs are classified as stromalTILs or intratumoral TILs.

In some embodiments, an individual cell of the collection of individualcells (e.g., an individual TIL) may be classified (e.g., via apreviously trained deep-learning algorithm) as a stromal TIL or anepithelial TIL by, first, defining a stroma (e.g., stromal region) andan epithelium (e.g., epithelial region) of the digitized H&E slide ofthe AA patient (or the tumor region). The individual cell of thecollection of individual cells may be classified as a stromal TIL if thecoordinates of the centroid of the individual cell of the collection ofindividual cells is within the stroma. On the other hand, if thecoordinates of the centroid of the individual cell of the collection ofindividual cells is within the epithelium, the individual cell of thecollection of individual cells is defined as an epithelial TIL.Classifying the TILs into either stromal TILs or epithelial TILsincludes acquiring electronic data, reading from a computer file,receiving a computer file, reading from a computer memory, or othercomputerized activity not practically performed in the human mind.

In some embodiments, a histologist may define the tumor region (see,e.g., second operation 104), define the boundaries of the plurality ofindividual cells (see, e.g., third operation 106), classify theplurality of individual cells into their cell types (see, e.g., fourthoperation 108), and/or classify the TILs as either stromal TILs orepithelial TILs (see, e.g., fifth operation 110). In other words, thehistologist may performed one or more of the second operation 104, thethird operation 106, the fourth operation 108, and the fifth operation110.

In other embodiments, the tumor region and/or the boundaries of theplurality of individual cells may be defined (e.g., generated) by animage segmentation technique, such as, a watershed segmentationtechnique, a region growing technique, an active contour technique, aconvolutional neural network (CNN), support vector machine (SVM)classifiers, some other image segmentation technique, or a combinationof the foregoing. It will be appreciated that the tumor region and/orthe boundaries of the plurality of individual cells may be generatedusing other image segmentations techniques.

In some embodiments, the image segmentation technique may also classifythe plurality of individual cells into their cell types and/or classifythe TILs as either stromal TILs or epithelial TILs. In other words, theimage segmentation technique may perform one or more of the secondoperation 104, the third operation 106, the fourth operation 108, andthe fifth operation 110.

In some embodiments, the image segmentation technique comprisesprocessing a whole slide image of the digitized H&E slide of the AApatient at 10 times (10X) magnification. The image segmentationtechnique may be trained to define the tumor region (see, e.g., secondoperation 104), define the boundaries of the plurality of individualcells (see, e.g., third operation 106), classify the plurality ofindividual cells into their cell types (see, e.g., fourth operation108), and/or classify the TILs as either stromal TILs or epithelial TILs(see, e.g., fifth operation 110). The image segmentation technique maybe trained on different types of tissue images (e.g., estrogen receptorpositive breast cancer tissue image patches) or the same type of tissueimages (e.g., digitized H&E slides of the uterus of other patients withEC). In some embodiments, the image segmentation technique assigns avalue to each pixel of the digitized H&E slide of the AA patient. Thevalue that is assigned to each pixel reflects the likelihood such apixel is part of the epithelium of the AA patient. In furtherembodiments, this probabilistic epithelial mask is converted to a binarymask by a likelihood threshold. Thus, the image segmentation techniquemay perform one or more of the second operation 104, the third operation106, the fourth operation 108, and the fifth operation 110.

It will be appreciated that a combination of image segmentationtechniques may be utilized to performed one or more of the secondoperation 104, the third operation 106, the fourth operation 108, andthe fifth operation 110. For example, in some embodiments, a watershedimage segmentation technique may be utilized to segment the nuclei ofthe plurality of individual cells (e.g., the second operation 104 and/orthe third operation 106). A SVM classifier then uses shape, texture,and/or color features of the nuclei to classify the plurality ofindividual cells into their respective cell types (see, e.g., fourthoperation 108) and/or classify the TILs as either stromal TILs orepithelial TILs (see, e.g., fifth operation 110).

The method 100 comprises a sixth operation 112. At the sixth operation112, one or more clusters of stromal TILs are generated. Each of the oneor more clusters of stromal TILs comprises a subset of stromal TILs thatare related to one another based on proximity. For example, based on theproximity of the stromal TILs in relation to one another, a firstcluster of TILs and a second cluster of TILs may be generated. The firstcluster of stromal TILs comprises a first subset of stromal TILs (e.g.,a first collection of stromal TILs) and the second cluster of stromalTILs comprises a second subset of stromal TILs (e.g., a secondcollection of stromal TILs). In some embodiments, the stromal TILs ofthe first subset of stromal TILs do not comprise any of the stromal TILsof the second subset of stromal TILs, or vice versa. In otherembodiments, some of the stromal TILs of the first subset of stromalTILs are also stromal TILs of the second subset of stromal TILs, or viceversa. In some embodiments, the one or more clusters of stromal TILs aregenerated via a graph theory technique. Generating the one or moreclusters of stromal TILs includes acquiring electronic data, readingfrom a computer file, receiving a computer file, reading from a computermemory, or other computerized activity not practically performed in thehuman mind.

In some embodiments, the one or more clusters of stromal TILs may begenerated by grouping the stromal TILs into corresponding clusters ofthe one or more clusters of stromal TILs based on a distance in whichthe stromal TILs are spaced from one another. In further embodiments,each stromal TIL of a given cluster of the one or more clusters ofstromal TILs is spaced from a neighboring stromal TIL of the givencluster by less than a threshold distance. For example, the stromal TILsmay comprise a first stromal TIL, a second stromal TIL, a third stromalTIL, a fourth stromal TIL, a fifth stromal TIL, and a sixth stromal TIL.The first, second, and third stromal TILs are grouped into a firstcluster of stromal TILs, and the fourth, fifth, and sixth stromal TILsare grouped into a second cluster of stromal TILs. The first, second,and third stromal TILs are grouped into the first cluster of stromalTILs because each of the first, second, and third stromal TILs arespaced from at least one other of the first, second, and third stromalTILs by less than the threshold distance. The fourth, fifth, and sixthstromal TILs are grouped into the second cluster of stromal TILs becauseeach of the fourth, fifth, and sixth stromal TILs are spaced from atleast one other of the fourth, fifth, and sixth stromal TILs by lessthan the threshold distance. Further, first, second, and third stromalTILs are grouped into the first cluster of stromal TILs and the fourth,fifth, and sixth stromal TILs are grouped into the second cluster ofstromal TILs because none of the first, second, or third stromal TILsare spaced from any of the fourth, fifth, or sixth stromal TILs by lessthan the threshold distance.

It will be appreciated that any number of clusters of TILs may begenerated (e.g., 1 cluster, 2 clusters, 3 clusters, 20 clusters, 100clusters, etc.). The number of clusters of stromal TILs is based on theproximity of the stromal TILs. For example, if any one of the first,second, or third stromal TILs were spaced from any of the fourth, fifth,or sixth stromal TILs by less than the threshold distance, the first,second, third, fourth, fifth, and sixth stromal TILs may be grouped intothe first cluster of stromal TILs. On the other hand, it will beappreciated that if the first, second, third, fourth, fifth, and sixthstromal TILs were spaced differently—keeping in mind the thresholddistance as described above—the first, second, third, fourth, fifth, andsixth stromal TILs may be grouped into three (or more) clusters ofstromal TILs.

The method 100 comprises a seventh operation 114. At the seventhoperation 114, a first plurality of architectural features are extractedfrom the digitized H&E slide image of the AA patient. The firstplurality of architectural features are at least partially based on thecluster of stromal TILs. In other words, the first plurality ofarchitectural features are architectural features of the TILs (ArcTILs)from the H&E slide of the AA patient.

The architectural features of the first plurality of architecturalfeatures are architectural features that have been determined to be(e.g., via a feature selection process, such as least absolute shrinkageand selection operator (LASSO), LASSO Cox regression, multivariable Coxregression model (MCRM), minimum redundancy maximum relevance (mRMR),best subsets selection, correlation feature selection, etc.) morerelevant (e.g., discriminative) architectural features for predictingoverall survival (OS) of AA patients with EC (e.g., the length of timefrom a given date that AA patients diagnosed with EC are still alive).Extracting the first plurality of architectural features includesacquiring electronic data, reading from a computer file, receiving acomputer file, reading from a computer memory, or other computerizedactivity not practically performed in the human mind.

In some embodiments, each of the first plurality of architecturalfeatures corresponds to a different architectural feature of the one ormore clusters of stromal TILs. For example, a first architecturalfeature of the first plurality of architectural features corresponds toa first architectural feature of the one or more clusters of stromalTILs, a second architectural feature of the first plurality ofarchitectural features corresponds to a second architectural feature ofthe one or more clusters of stromal TILs that is different than thefirst architectural feature of the one or more clusters of stromal TILs,a third architectural feature of the first plurality of architecturalfeatures corresponds to a third architectural feature of the one or moreclusters of stromal TILs that is different than both the first andsecond architectural features of the one or more clusters of stromalTILs, and so forth. In further embodiments, the first architecturalfeature may be a ratio of non-TILs (non-lymphocyte cells) density to asurrounding TIL one (e.g., a surrounding TIL density) in the epithelium(e.g., epithelial TILs and epithelial non-TILs). The secondarchitectural feature may be the number of TIL clusters that are nearby(e.g., within a threshold distance) of a given non-TIL cluster in theepithelium. The third architectural feature may be the percentage ofstromal non-TILs that are disposed within a twenty (20) micrometerproximity of a given non-TIL cluster.

In some embodiments, the first plurality of architectural features arebased on the one or more clusters of stromal TILs. In furtherembodiments, the one or more clusters of stromal TILs may comprise onlyone cluster of stromal TILs and the first plurality of architecturalfeatures may be based only on the one cluster of stromal TILs. In someembodiments, the first plurality of architectural features may only bebased on the one or more clusters of stromal TILs. In some embodiments,the first plurality of architectural features comprises at least fourarchitectural features of the one or more clusters of stromal TILs. Insome embodiments, the first plurality of architectural features consistsof four architectural features of the one or more clusters of stromalTILs (e.g., the first plurality of architectural features includes onlyfour architectural features of the one or more clusters of stromalTILs).

The method 100 comprises an eighth operation 116. At the eighthoperation 116, a risk score is generated for the AA patient based on thefirst plurality of architectural features. In some embodiments,generating the risk score comprises assigning a value to each of thearchitectural features of the first plurality of architectural features.In other words, a plurality of values are assigned to the plurality ofarchitectural features, respectively. In further embodiments, the valuesare based on the number of times a specific indicator (e.g., adifference in pixel intensity, pixel intensity within a predefinedrange, etc.) occurs in the digitized H&E slide of the AA patient. In yetfurther embodiments, the values are based on the number of times aspecific indicator (e.g., a difference in pixel intensity, pixelintensity within a predefined range, etc.) occurs in the one or moreclusters of stromal TILs.

In some embodiments, the values are based on the number of times acorresponding architectural feature of the plurality of architecturalfeatures is present in the digitized H&E slide image of the AA patient.In further embodiments, the values are based on the number of times acorresponding architectural feature of the plurality of architecturalfeatures is present in the one or more clusters of stromal TILs.

For example, the first architectural feature may be present ten times inthe one or more clusters of stromal TILs (or the digitized H&E slideimage of the AA patient). Thus, for the digitized H&E slide image of theAA patient, a value of ten is assigned to the first architecturalfeature. The second architectural feature may be present twenty times inthe one or more clusters of stromal TILs (or the digitized H&E slideimage of the AA patient). Thus, for the digitized H&E slide image of theAA patient, a value of twenty is assigned to the second architecturalfeature. It will be appreciated that different values, which are stillbased on the number of times a corresponding architectural feature ofthe plurality of architectural features is present in the digitized H&Eslide image of the AA patient (or the one or more clusters of stromalTILs), may be assigned to the first plurality of architectural features(e.g., the assigned values may be normalized). For example, even thoughthe first architectural feature may be present ten times in the one ormore clusters of stromal TILs and the second architectural feature maybe present twenty times in the one or more clusters of stromal TILs, avalue of two may be assigned to the second architectural feature and avalue of one may be assigned to the first architectural feature for thedigitized H&E slide image of the AA patient (e.g., two to one isequivalent to twenty to ten).

In some embodiments, generating the risk score may include weighting thearchitectural features based on corresponding coefficients (e.g., thevalues are multiplied by respective weighting coefficients). In otherwords, a weighting coefficient is assigned to each of the values. Inother words, the weighting coefficients are attached to the values,respectively. In some embodiments, each value is multiplied by itsrespective weighting coefficient to generate a plurality of weightedvalues.

The weighting coefficients may be generated by the feature selectionmodel. For example, in some embodiments, the weighting coefficients aregenerated by a LASSO technique (e.g., the weights are selected by aLASSO feature selection model). In some embodiments, the architecturalfeatures of the first plurality of architectural features arestandardized. For example, in some embodiments, the architecturalfeatures of the first plurality of architectural features are firststandardized to have a mean of zero and a standard deviation of one(relative to a training dataset/cohort) so that hazard ratio (HR) andfeature weights (e.g., weighting coefficients) are comparable across thefirst plurality of architectural features.

The values (or weighted values) are then combined to generate the riskscore for the AA patient. The values (or weighted values) are combinedbased on a function. The function may comprise combining the values (orweighted values) by, for example, addition, subtraction, multiplication,division, some other mathematical operator, or a combination of theforegoing. In some embodiments, the risk score is generated based on alinear combination of the values (or weighted values). In other words,the values (or weighted values) are combined linearly to generate therisk score for the AA patient.

The risk score may be a number (e.g., a numerical value) that is basedon the combination of the values (or weighted values). The risk score isprognostic of the OS of the AA patient (e.g., the risk score ispredictive of OS of the AA patient). In some embodiments, the risk scoreis generated by using a LASSO technique. Generating the risk score forthe AA patient includes acquiring electronic data, reading from acomputer file, receiving a computer file, reading from a computermemory, or other computerized activity not practically performed in thehuman mind.

The method 100 comprises a ninth operation 118. At the ninth operation118, a risk group classification for the AA patient is generated.Generating the risk group classification comprises classifying the AApatient into either a high risk group or a low risk group based, atleast partially, on the risk score. In some embodiments, the high riskgroup indicates the AA patient will die before a threshold date, and thelow risk group indicates the AA patient will die after or on thethreshold date. In other embodiments, the high risk group indicates theAA patient will die on or before the threshold date, and the low riskgroup indicates the AA patient will die after the threshold date. Insome embodiments, the threshold date is a predefined time (e.g., days,months, etc.) from either the date the AA patient is diagnosed with ECor the date the AA patient starts a treatment plan for their EC to thedate in which the AA patient dies. Generating the risk groupclassification for the AA includes acquiring electronic data, readingfrom a computer file, receiving a computer file, reading from a computermemory, or other computerized activity not practically performed in thehuman mind.

In other embodiments, the high risk group indicates a probability thatthe AA patient will die within a date range is greater than a thresholdprobability, and the low risk group indicates the probability that theAA patient will die within the date range is less than or equal to thethreshold probability. In other embodiments, the high risk groupindicates the probability that the AA patient will die within the daterange is greater than or equal to the threshold probability, and the lowrisk group indicates the probability that the AA patient will die withinthe date range is less than the threshold probability. For example, if agiven AA patient is classified into the high risk group, the given AApatient may be twice as likely to die with 12 months than if the givenAA patient was classified into the low risk group. As another example,if a given AA patient is classified into the high risk group, the givenAA patient may be five times as likely to die with 36 months than if thegiven AA patient was classified into the low risk group.

In some embodiments, the date range is a predefined range of months(e.g., 12 months, 36 months, etc.). The date range may begin with eitherthe date (e.g., the day) the AA patient is diagnosed with EC or the datethe AA patient starts a treatment plan for their EC. In someembodiments, the threshold probability is a predefined probabilityvalue. In other embodiments, the threshold probability may be apredefined range of probabilities. In such embodiments, the high riskgroup may indicate the probability that the AA patient will die withinthe date range is greater than (or greater than or equal to) a maximumprobability of the range of probabilities, and the low risk group mayindicate the probability that the AA patient will die within the daterange is less than or equal to (or less than) the minimum probability ofthe range of probabilities.

In some embodiments, classifying the AA patient into either the highrisk group or the low risk group comprises comparing the risk score ofthe AA patient to a threshold risk score value. For example, in someembodiments, if the risk score of the AA patient is greater than thethreshold risk score value, the AA patient is classified into the highrisk group. In further embodiments, if the risk score of the AA patientis less than or equal to the threshold risk score value, the AA patientis classified into the low risk group. In other embodiments, if the riskscore of the AA patient is greater than or equal to the threshold riskscore value, the AA patient is classified into the high risk group; andif the risk score of the AA patient is less than the threshold riskscore value, the AA patient is classified into the low risk group. Inyet other embodiments, if the risk score of the AA patient is greaterthan (or equal to) the threshold risk score value, the AA patient isclassified into the low risk group; and if the risk score of the AApatient is less than or equal to (or less than) the threshold risk scorevalue, the AA patient is classified into the high risk group.

In some embodiments, the threshold risk score value is the medianthreshold risk score value of a group of other AA patients (e.g., atraining dataset). In further embodiments, the AA patient may beclassified into either the high risk group or the low risk group bycomparing the risk score of the AA patient to the threshold risk scorevalue due to a statistical model indicating that the risk score issignificantly associated with OS of AA patient's (e.g., a Cox regressionmodel produced a statistically significant result that indicated therisk score of a AA patient corresponds to the OS of the AA patient).

The method 100 comprises a tenth operation 120. At the tenth operation120, the risk group classification of the AA patient is displayed. Therisk group classification may be displayed on, for example, a computermonitor, a smartphone display, a tablet display, or some other displaydevice, or a combination of the foregoing. It will be appreciated thatthe risk group classification may be displayed in other mediums (e.g.,the classification may be printed on paper) in addition to, or in lieuof, displaying the risk group classification on a display device.

In some embodiments, the risk group classification may be displayedalong with displaying one or more of the first plurality ofarchitectural features of the AA patient (e.g., the values (or weightedvalues)), the risk score of the AA patient, the digitized H&E slide ofthe AA patient, some other classification of the patient (e.g., responseof the AA patient to a treatment plan for the AA patient's EC,classification of the EC of the patient as aggressive or non-aggressive,etc.), or a combination of the foregoing.

In some embodiments, displaying the risk group classification alsoincludes controlling a personalized medicine system, a computer monitor,or other display, to display operating parameters or characteristics ofa machine learning classifier, during at least one of training andtesting of the machine learning classifier, or during clinical operationof the machine learning classifier. In some embodiments, displaying therisk group classification comprises selecting for the risk groupclassification to be displayed via a graphical control element (e.g., byclicking/tapping on an item in a drop-down list). Displaying the riskgroup classification includes acquiring electronic data, reading from acomputer file, receiving a computer file, reading from a computermemory, or other computerized activity not practically performed in thehuman mind.

By displaying the risk group classification, a medical practitioner maybe able to easily and timely (e.g., intuitively due to the singleclassification being displayed) determine the time in which the AApatient has to live. Accordingly, the medical practitioner may be ableto accurately guide the EC treatment of the AA patient to achieve bettertreatment results (e.g., expedite alternative treatment options (e.g.,adjuvant therapy), choose a less aggressive treatment plan to reducenegative side effects, etc.). Further, the medical practitioner may beable to provide better care to the AA patient (e.g., improve patientsatisfaction and/or knowledge) by being able to better predict lifeexpectancy and provide this information to the AA patient.

FIG. 2 illustrates some embodiments of a method 200 for generating aprediction to a response to a treatment plan for EC for an AA patientwith EC. In some embodiments, the AA patient is referred to as a AA POI(e.g., due to the method 200 generating a prediction for the AApatient). The method 200 comprises operations 102-116 as describedherein.

The method 200 comprises a first operation 202. At the first operation202, the risk score is provided to a machine learning classifier. Themachine learning classifier is trained to predict a response of the AApatient to a treatment plan for EC. The treatment plan comprises atleast one of chemotherapy and radiotherapy. It will be appreciated thatthe treatment plan may comprise additional treatments, such as hormonetherapy, targets therapy, biological therapy, or some other type oftherapy. The machine learning classifier predicts the response of the AApatient to the treatment plan (e.g., chemoradiation) based on, at leastin part, the risk score of the AA patient.

In some embodiments, the machine learning classifier may be, forexample, a quadratic discriminant analysis (QDA) classifier, a supportvector machine (SVM) classifier, a linear discriminant analysis (LDA)classifier, or some other machine learning classifier. Providing therisk score of the AA patient to the machine learning classifier includesacquiring electronic data, reading from a computer file, receiving acomputer file, reading from a computer memory, or other computerizedactivity not practically performed in the human mind.

The method 200 comprises a second operation 204. At the second operation204, a classification of the AA patient into either a responder group(RG) or a non-responder group (NRG) is received from the machinelearning classifier. The RG indicates that the AA patient will respondto the treatment plan (e.g., the treatment plan will improve (e.g.,eliminate, reduce, etc.) the EC of the AA patient. The NRG indicatesthat the AA patient will not respond to the treatment plan (e.g., thetreatment plan will not improve the EC of the AA patient). The machinelearning classifier classifies the AA patient into either the RG or theNRG based, at least in part, on the risk score of the AA patient (e.g.,the machine learning classifier has been trained to predict a responseto the treatment plan by classifying the AA patient into either the RGor the NRG based, at least in part, on the risk score of the AApatient). Receiving the classification of the AA patient as either inthe NRG or in the RG includes acquiring electronic data, reading from acomputer file, receiving a computer file, reading from a computermemory, or other computerized activity not practically performed in thehuman mind.

In some embodiments, the machine learning classifier classifies the AApatient into either the RG or the NRG by generating a classificationvalue (e.g., a numerical value) based, at least in part, on the riskscore of the AA patient. For example, in some embodiments, if theclassification value (e.g., the numerical value) is less than (orgreater than) a threshold classification value, the machine learningclassifier classifies the AA patient into the NRG. On the other hand, ifthe classification value is greater than or equal to (or less than orequal to) the threshold classification value, the machine learningclassifier classifies the AA patient into the RG. In other embodiments,if the classification value is less than or equal to (or greater than orequal to) the threshold classification value, the machine learningclassifier classifies the AA patient into the NRG; and if theclassification value is greater than (or less than) the thresholdclassification value, the machine learning classifier classifies the AApatient into the RG. It will be appreciated that other classificationtechniques may be employed.

The machine learning classifier classifies the AA patient into eitherthe RG or the NRG based on the risk score of the AA patient (e.g., themachine learning classifier has been trained to predict a response tothe treatment plan by classifying the AA patient into either the RG orthe NRG based on the risk score of the AA patient). For example, themachine learning classifier generates the classification value based onthe risk score of the patient.

In some embodiments, the machine learning classifier may classify the AApatient into either the RG or the NRG based on the risk score of the AApatient and at least one other feature that is prognostic of the OS ofthe AA patient (e.g., the machine learning classifier has been trainedto predict a response to the treatment plan by classifying the AApatient into either the RG or the NRG based on both the risk score andthe at least one other feature). For example, the machine learningclassifier generates the classification value based on a combination ofthe risk score of the AA patient and the at least one other feature thatis prognostic of the OS of the AA patient. The at least one otherfeature that is prognostic of the OS of the AA patient is different thanthe risk score. For example, the at least one other feature that isprognostic of the OS of the AA patient is a stage of the AA patient'sEC. The stage of the AA patient's EC may be determined by a medicalpractitioner (e.g., chemotherapist) and/or by a processor configured togenerate the AA patient's EC stage. The AA patient's EC stage may begenerated before, after, or concurrently with generating the risk scoreof the AA patient.

The method 200 comprises a third operation 206. At the third operation206, the classification of the AA patient as either in the NRG or in theRG is displayed.

The classification of the AA patient as either in the NRG or in the RGmay be displayed on, for example, a computer monitor, a smartphonedisplay, a tablet display, or some other display device, or acombination of the foregoing. It will be appreciated that theclassification of the AA patient as either in the NRG or in the RG maybe displayed in other mediums (e.g., the classification may be printedon paper) in addition to, or in lieu of, displaying the classificationon a display device.

In some embodiments, the classification of the AA patient as either inthe NRG or in the RG may be displayed along with displaying one or moreof the first plurality of architectural features of the AA patient(e.g., the values (or weighted values)), the risk score of the AApatient, the H&E slide of the AA patient, some other classification ofthe patient (e.g., the risk group classification of the AA (see, e.g.,tenth operation 120)), or a combination of the foregoing.

In some embodiments, displaying the classification of the AA patient aseither in the NRG or in the RG also includes controlling a personalizedmedicine system, a computer monitor, or other display, to displayoperating parameters or characteristics of a machine learningclassifier, during at least one of training and testing of the machinelearning classifier, or during clinical operation of the machinelearning classifier. In some embodiments, displaying the classificationof the AA patient as either in the NRG or in the RG comprises selectingfor the classification of the AA patient into either the NRG or the RGto be displayed via a graphical control element (e.g., byclicking/tapping on an item in a drop-down list). Displaying theclassification of the AA patient as either in the NRG or in the RGincludes acquiring electronic data, reading from a computer file,receiving a computer file, reading from a computer memory, or othercomputerized activity not practically performed in the human mind.

By displaying the classification of the AA patient as either in the NRGor in the RG, a medical practitioner may be able to easily and timelypredict the AA patient's response to the treatment plan. Accordingly,the medical practitioner may be able to accurately guide the treatmentof the AA patient to achieve better treatment results (e.g., maintainthe treatment plan, change the treatment plan to expedite alternativetreatment options, etc.).

FIG. 3 illustrates some embodiments of a method 300 for generating botha prediction for OS of an AA patient with EC and a prediction to aresponse to a treatment plan for EC for the AA patient with EC. Themethod 300 comprises operations 102-116 and 202-206 as described herein.

As shown in the method 300 of FIG. 3 , a risk score is generated for anAA patient based on a first plurality of architectural features (see,e.g., the eighth operation 116). A risk group classification for the AApatient is generated (see, e.g., the ninth operation 118). The riskgroup classification of the AA patient is displayed (see, e.g., thetenth operation 120). Further, the risk score of the AA patient isprovided to a machine learning classifier that is trained to predict aresponse of the AA patient to a treatment plan for EC (see, e.g., thefirst operation 202). A classification of the AA patient into either aresponder group or a non-responder group is received from the machinelearning classifier (see, e.g., the second operation 204). Theclassification of the AA patient as either in the non-responder group orthe responder group is displayed (see, e.g., the third operation 206).

In some embodiments, both the classification of the AA patient as eitherin the non-responder group or the responder group and the risk groupclassification of the AA patient are displayed. In other embodiments, atleast one of the classification of the AA patient as either in thenon-responder group or the responder group and the risk groupclassification of the AA patient is displayed.

FIG. 4 illustrates some embodiments of a method 400 for classifying anAA patient's EC as being an aggressive subtype of EC or a non-aggressivesubtype of EC. In some embodiments, the AA patient is referred to as aAA POI (e.g., due to the method 400 generating a prediction for the ECof the AA patient). The method 400 comprises operations 102-116 asdescribed herein.

The method 400 comprises a first operation 402. At the first operation402, the risk score is provided to a machine learning classifier. Themachine learning classifier is trained to predict whether the EC of theAA patient is either an aggressive subtype of EC or a non-aggressivesubtype of EC (also referred to as “predict(s) the EC subtype of the AApatient”). The machine learning classifier predicts the EC subtype ofthe AA patient based on, at least partially, the risk score of the AApatient. In some embodiments, the machine learning classifier is trainedto predict a difference between an aggressive subtype of endometrialcancer (EC) and a non-aggressive subtype of EC. In such embodiments, themachine learning classifier may utilize the prediction of the differencebetween the aggressive subtype of EC and the non-aggressive subtype ofEC to predict the EC subtype of the AA patient. In further suchembodiments, training the machine learning classifier to predict thedifference between the aggressive subtype of EC and the non-aggressivesubtype of EC is based on architectural features corresponding to thefirst plurality of architectural features being extracted from otherpatients with EC (e.g., determining that the first plurality ofarchitectural features are more predictive of correctly classifying thesubtype of EC of AA patients of a training dataset than they are forclassifying the subtype of EC of CA patients of the training dataset).

In some embodiments, the machine learning classifier may be, forexample, a QDA classifier, a SVM classifier, a LDA classifier, or someother machine learning classifier. Providing the risk score of the AApatient to the machine learning classifier includes acquiring electronicdata, reading from a computer file, receiving a computer file, readingfrom a computer memory, or other computerized activity not practicallyperformed in the human mind.

The method 400 comprises a second operation 404. At the second operation404, a classification of the EC of the AA patient as/into either theaggressive subtype of EC or the non-aggressive subtype of EC is receivedfrom the machine learning classifier. The machine learning classifierclassifies the subtype of the EC of the AA patient based on the firstplurality of architectural features of the AA patient.

In some embodiments, the machine learning classifier classifies the ECof the AA patient by generating a classification value (e.g., anumerical value) based, at least in part, on the risk score of the AApatient. It will be appreciated that other classification techniques mayalso be employed. Receiving the classification of the EC of the AApatient as either the aggressive subtype of EC or the non-aggressivesubtype of EC includes acquiring electronic data, reading from acomputer file, receiving a computer file, reading from a computermemory, or other computerized activity not practically performed in thehuman mind.

The method 400 comprises a third operation 406. At the third operation406, the classification of the AA patient as either in the non-respondergroup or in the responder group is displayed. In other words, theclassification of the AA patient's EC is displayed.

The classification of the AA patient's EC may be displayed on, forexample, a computer monitor, a smartphone display, a tablet display, orsome other display device, or a combination of the foregoing. It will beappreciated that the classification of the AA patient's EC may bedisplayed in other mediums (e.g., the classification may be printed onpaper) in addition to, or in lieu of, displaying the classification on adisplay device.

In some embodiments, the classification of the AA patient's EC may bedisplayed along with displaying one or more of the first plurality ofarchitectural features of the AA patient (e.g., the values (or weightedvalues)), the risk score of the AA patient, the H&E slide of the AApatient, one or more of other classifications of the patient (e.g., therisk group classification of the AA (see, e.g., tenth operation 120),the classification of the AA as either in a NRG or a RG (see, e.g.,third operation 206)), or a combination of the foregoing.

In some embodiments, displaying the classification of the AA patient'sEC also includes controlling a personalized medicine system, a computermonitor, or other display, to display operating parameters orcharacteristics of a machine learning classifier, during at least one oftraining and testing of the machine learning classifier, or duringclinical operation of the machine learning classifier. In someembodiments, displaying the classification of the AA patient's ECcomprises selecting for the classification of the AA patient's EC to bedisplayed via a graphical control element (e.g., by clicking/tapping onan item in a drop-down list). Displaying the classification of the AApatient's EC includes acquiring electronic data, reading from a computerfile, receiving a computer file, reading from a computer memory, orother computerized activity not practically performed in the human mind.

By displaying the classification of the EC of the AA patient, a medicalpractitioner may be able to easily and timely predict the aggressivenessof the AA patient's EC. Accordingly, the medical practitioner may beable to accurately guide the treatment of the AA patient to achievebetter treatment results.

FIG. 5 illustrates a method 500 of some more detailed embodiments of theseventh operation 114 of the method 100 of FIG. 1 . In other words, themethod 500 illustrates some more detailed embodiments of extracting afirst plurality of architectural features from the digitized H&E slideof the AA patient.

As shown in the method 500, in some embodiments, extracting the firstplurality of architectural features from the digitized H&E slide of theAA patient comprises a first operation 502. At the first operation 502,a second plurality of architectural features are extracted from thedigitized H&E slide of the AA patient. Extracting the second pluralityof architectural features includes acquiring electronic data, readingfrom a computer file, receiving a computer file, reading from a computermemory, or other computerized activity not practically performed in thehuman mind.

At least some of the architectural features of the second plurality ofarchitectural features are extracted from the tumor region of thedigitized H&E slide of the AA patient. At least some of thearchitectural features of the second plurality of architectural featuresare extracted from the one or more clusters of stromal TILs. At leastsome of the architectural features of the second plurality ofarchitectural features may be extracted from one or more clusters ofepithelial TILs (e.g., substantially similar to (and substantiallysimilarly generated as) the one or more clusters of stromal TILs). Atleast some of the architectural features of the second plurality ofarchitectural features may be extracted from the TILs of the pluralityof individual cells. At least some of the architectural features of thesecond plurality of architectural features may be extracted from thenon-lymphocyte cells (e.g., stromal non-lymphocyte cells) of theplurality of individual cells. At least some of the architecturalfeatures of the second plurality of architectural features may beextracted from the cancer cells of the plurality of individual cells.

The method 500 further comprises a second operation 504. At the secondoperation 504, a subset of architectural features of the secondplurality of architectural features are selected. The subset ofarchitectural features define the first plurality of architecturalfeatures. In other words, the first plurality of architectural featuresconsist of the subset of architectural features of the second pluralityof architectural features.

The subset of architectural features are selected from the secondplurality of architectural features by determining which architecturalfeatures of the second plurality of architectural features are morerelevant (e.g., the most discriminative) for predicting overall survival(OS) of AA patients with EC. A feature selection process determineswhich architectural features of the second plurality of architecturalfeatures are more relevant (e.g., discriminative) for predicting OS ofAA patients with EC. The architectural features of the second pluralityof architectural features that are found (e.g., via the featureselection process) to be more relevant (e.g., the most discriminative)for predicting OS of AA patients with EC are selected as the subset ofarchitectural features.

In some embodiments, the feature selection process may be, for example,LASSO, LASSO Cox regression, multivariable Cox regression model (MCRM),mRMR, best subsets selection, correlation feature selection, or thelike. In further embodiments, the feature selection is LASSO Coxregression. In embodiments in which the feature selection is LASSO Coxregression, the subset of architectural features may be more relevantthan another, different subset of architectural features of the secondplurality of architectural features which were selected by a differentfeature selection process (e.g., mMRM). Selecting the subset ofarchitectural features includes acquiring electronic data, reading froma computer file, receiving a computer file, reading from a computermemory, or other computerized activity not practically performed in thehuman mind.

FIG. 6 illustrates a method 600 of some other embodiments of the methodof FIG. 2 . The method 600 is similar to the method 200 of FIG. 2 andincludes operations 102-116 and 202-206, but also includes a firstoperation 602. At the first operation 602, a machine learning classifieris trained to generate a classification of an AA patient with EC intoeither a responder group or a non-responder group (e.g., the RG and NRGdescribed in reference to method 200). In some embodiments, the AApatient is referred to as an AA POI (e.g., due to the method 600generating a prediction for the AA patient).

While not shown explicitly in figures, it will be appreciated that themethod 100 of FIG. 1 , the method 300 of FIG. 3 , and the method 400 ofFIG. 4 may also comprise a corresponding operation of training a machinelearning classifier. For example, the method 100 of FIG. 1 may comprisean operation of training a machine learning classifier to generate arisk group classification (e.g., the risk group classification describedin reference to method 100). In some embodiments, the method 300 of FIG.3 may comprise an operation of training a machine learning classifier togenerate a risk group classification (e.g., the risk groupclassification described in reference to method 100) and training themachine learning classifier to generate a classification of an AApatient with EC into either a responder group or a non-responder group.In some embodiments, the method 400 of FIG. 4 may comprise an operationof training a machine learning classifier to predict whether the EC ofthe AA patient is either an aggressive subtype of EC or a non-aggressivesubtype of EC (e.g., the aggressive subtype of EC or the non-aggressivesubtype of EC described in reference to method 400).

FIG. 7 illustrates a method 700 of some embodiments of the firstoperation 602 of the method 600 of FIG. 6 . In other words, the method700 illustrates some embodiments of training a machine learningclassifier to generate a classification of an AA patient with EC (alsoknown as a AA POI) into either a responder group or a non-respondergroup.

The method 700 comprises a first operation 702. At the first operation702, a training dataset of digitized H&E slide images is accessed. Thetraining dataset comprises a plurality of digitized H&E training slideimages (e.g., data from a plurality of digitized H&E training slideimages). Each of the plurality of digitized H&E training slide imagesdemonstrates tissue from a uterus of a corresponding AA patient and aportion of a gynecologic tumor of the corresponding AA patient. Further,each of the plurality of digitized H&E training slide images isassociated with a past AA patient that has EC.

For example, the plurality of digitized H&E training slide imagescomprises a first digitized H&E training slide image, a second digitizedH&E training slide image, and so forth. The first digitized H&E trainingslide image is associated with a first past AA patient with EC (e.g., afirst AA woman who was diagnosed with EC at an earlier time), the seconddigitized H&E training slide image is associated with a second past AApatient with EC (e.g., a second (different) AA woman who was diagnosedwith EC at an earlier time), and so forth. Accessing the trainingdataset includes acquiring electronic data, reading from a computerfile, receiving a computer file, reading from a computer memory, orother computerized activity not practically performed in the human mind.

The method 700 comprises a second operation 704. At the second operation704, a tumor region (also referred to as AA tumor region) in each of thedigitized H&E slides of the AA patients is defined. Each tumor regioncomprises a plurality of individual cells of a corresponding one of theAA patients. For example, the tumor region of the first digitized H&Etraining slide image comprises a plurality of individual cells of thefirst past AA patient, the tumor region of the second digitized H&Etraining slide image comprises a plurality of individual cells of thesecond past AA patient, and so forth. In some embodiments, the pluralityof individual cells of the past AA patients are referred to as theplurality of AA cells. For example, the plurality of individual cells ofthe first past AA patient may be referred to as a (first) plurality ofAA cells, the plurality of individual cells of the second past AApatient may be referred to as a (second) plurality of AA cells, and soforth. In further embodiments, the plurality of individual cells of thepast AA patients (the plurality of AA cells of the past AA patients) maybe collectively referred to as the pluralities of individual cells ofthe past AA patients (the pluralities of AA cells of the past AApatients).

The second operation 704 is substantially similar (e.g., having the samegeneral process) to the second operation 104, except the secondoperation 704 defines a tumor region for each of the digitized H&Eslides of the AA patients (each of the plurality of digitized H&Etraining slide images), whereas the second operation 104 defines a tumorregion for a digitized H&E slide image of an AA patient with EC (alsoreferred to as “a digitized H&E slide image of an AA POI”).

The method comprises 700 a third operation 706. At the third operation706, each of the AA cells are classified as one cell type of a pluralityof cell types. The plurality of cell types comprises tumor-infiltratinglymphocytes (TILs), non-lymphocyte cells, and cancer cells. The thirdoperation 706 is substantially similar to the fourth operation 108,except the third operation 706 classifies each of the AA cells of eachof the plurality of AA cells of the past AA patients to a specific celltype, whereas the fourth operation 108 classifies the plurality ofindividual cell of the patient with EC (also referred to as “theplurality of AA cells of the AA POI”).

The method 700 comprises a fourth operation 708. At the fourth operation708, for each of the corresponding plurality of AA cells, one or moreclusters of AA stromal TILs and one or more clusters of AA epithelialTILs are generated. For example, one or more clusters of AA stromal TILsand one or more clusters of AA epithelial TILs are generated for theplurality of AA cells of the first past AA patient, one or more clustersof AA stromal TILs and one or more clusters of AA epithelial TILs aregenerated for the plurality of AA cells of the second past AA patient,and so forth.

The fourth operation 708 is substantially similar to the sixth operation112, except the fourth operation 708 generates one or more clusters ofAA stromal TILs and one or more clusters of AA epithelial TILs for eachof the pluralities of AA cells, whereas the sixth operation 112generates one or more clusters of AA stromal TILs (and generating one ormore clusters of AA epithelial TILs in a similar manner as the one ormore clusters of AA stromal TILs are generated, for example, based onproximity) for the plurality of individual cell of the patient with EC(also referred to as “the plurality of AA cells of the AA POI”).

In some embodiments, the method 700 may also comprises defining aboundary for each of the AA cells of each of the correspondingpluralities of AA cells (see, e.g., third operation 106 of the method100 of FIG. 1 ). In such embodiments, the clusters of the one or moreclusters of stromal TILs, one or more clusters of stromal non-lymphocytecell, one or more clusters of epithelial TILs, one or more clusters ofcancer cells, and the like, may be based on grouping cells of the sametype (e.g., stromal TILs) based on proximity. In further suchembodiments, the proximity of cells may be based on a distance in whichboundaries of neighboring cells of the same type are spaced from oneanother (e.g., less than or greater than a threshold distance).

The method comprises 700 a fifth operation 710. At the fifth operation710, for each of the corresponding plurality of AA cells, a firstplurality of architectural features are extracted from the one or moreclusters of AA stromal TILs. In other words, for each of thecorresponding plurality of AA cells, a first plurality of architecturalfeatures are extracted from the digitized H&E training slide images,where the first plurality of architectural features are at leastpartially based on corresponding ones of the one or more clusters ofstromal TILs. For example, a first plurality of architectural featuresare extracted from the one or more clusters of stromal TILs that weregenerated for the first past AA patient, a first plurality ofarchitectural features are extracted from the one or more clusters ofstromal TILs that were generated for the second past AA patient, and soforth.

In some embodiments, the first plurality of architectural features arecollectively referred to as (e.g., each of the first plurality ofarchitectural features are collectively referred to as) firstpluralities of architectural features. For example, the firstpluralities of architectural features comprise the first plurality ofarchitectural features that were extracted from the one or more clustersof stromal TILs that were generated for the first past AA patient, thefirst plurality of architectural features that were extracted from theone or more clusters of stromal TILs that were generated for the secondpast AA patient, and so forth.

The fifth operation 710 is substantially similar to the seventhoperation 114, except the fifth operation 710 comprises extracting afirst plurality of architectural features from the one or more clustersof AA stromal TILs for each of the corresponding plurality of AA cells,whereas the seventh operation 114 extracts a first plurality ofarchitectural features from the digitized H&E slide image of the AApatient (also referred to as “the digitized H&E slide image of the AAPOI”).

The method comprises 700 a sixth operation 712. At the sixth operation712, for each of the corresponding plurality of AA cells, a secondplurality of architectural features are extracted from the one or moreclusters of AA epithelial TILs. In other words, for each of thecorresponding plurality of AA cells, a second plurality of architecturalfeatures are extracted from the digitized H&E training slide images,where the second plurality of architectural features are at leastpartially based on corresponding ones of the one or more clusters ofepithelial TILs. For example, a second plurality of architecturalfeatures are extracted from the one or more clusters of epithelial TILsthat were generated for the first past AA patient, a second plurality ofarchitectural features are extracted from the one or more clusters ofepithelial TILs that were generated for the second past AA patient, andso forth.

In some embodiments, the second plurality of architectural features arecollectively referred to as (e.g., each of the second plurality ofarchitectural features are collectively referred to as) secondpluralities of architectural features. For example, the secondpluralities of architectural features comprise the second plurality ofarchitectural features that were extracted from the one or more clustersof epithelial TILs that were generated for the first past AA patient,the second plurality of architectural features that were extracted fromthe one or more clusters of epithelial TILs that were generated for thesecond past AA patient, and so forth.

The sixth operation 712 is substantially similar to the fifth operation710, except the sixth operation 712 extracts a second plurality ofarchitectural features from the one or more clusters of AA epithelialTILs for each of the corresponding plurality of AA cells, whereas thefifth operation 710 extracts a first plurality of architectural featuresfrom the one or more clusters of AA stromal TILs for each of thecorresponding plurality of AA cells.

The method comprises 700 a seventh operation 714. At the seventhoperation 714, the first pluralities of architectural features and thesecond pluralities of architectural features are refined to a thirdplurality of architectural features. The architectural features of thethird plurality of architectural features are more relevant topredicting OS of the AA patients than the other architectural featuresof the first pluralities of architectural features and the secondpluralities of architectural features.

For example, each of the first pluralities of architectural features maycomprise 40 different architectural features that were extracted fromthe one or more clusters of AA stromal TILs of a corresponding AApatient (e.g., a corresponding AA patient from the training cohort).Likewise, each of the second pluralities of architectural features maycomprise 45 different architectural features that were extracted fromthe one or more clusters of AA epithelial TILs of a corresponding AApatient (e.g., a corresponding AA patient from the training cohort). Inother words, for each of the AA patients with EC (training cohort AApatients), 85 different architectural features may be extracted fromeach of the digitized H&E training slide images (e.g., 40 differentarchitectural features from clusters of stromal TILs and 45 differentarchitectural features from clusters of epithelial TILs). Thearchitectural features of the third plurality of architectural featuresare more relevant (e.g., more discriminative) of the AA patients thanthe other architectural features of the first pluralities ofarchitectural features and the second pluralities of architecturalfeatures

A feature selection process determines which architectural features ofthe AA patients are more relevant to OS of the AA patients than theother architectural features of the first pluralities of architecturalfeatures and the second pluralities of architectural features. In someembodiments, the feature selection process may be, for example, LASSO,LASSO Cox regression, MCRM mRMR, best subsets selection, correlationfeature selection, or the like. In further embodiments, the featureselection is MCRM. In embodiments in which the feature selection isMCRM, the third plurality of architectural features may be more relevantthan another, different refined plurality of architectural features ofthe second and third pluralities of architectural features which wereselected by a different feature selection process (e.g., best subsets).In further embodiments, refining the first pluralities of architecturalfeatures and the second pluralities of architectural features to thethird plurality of architectural features includes acquiring electronicdata, reading from a computer file, receiving a computer file, readingfrom a computer memory, or other computerized activity not practicallyperformed in the human mind.

In some embodiments, the third plurality of architectural featurescomprises architectural features from only the first plurality ofarchitectural features. In other words, each of the architecturalfeatures of the third plurality of architectural features corresponds toonly one of the architectural features of the first plurality ofarchitectural features. More specifically, each of the architecturalfeatures of the third plurality of architectural features corresponds toan architectural feature that was extracted from the one or moreclusters of AA stromal TILs. For example, the first plurality ofarchitectural features is extracted from each of the past AA patients.Each of the architectural features of the first plurality ofarchitectural features are based on the one or more clusters of stromalTILs of a corresponding past AA patient. The architectural features ofthe third plurality of architectural features may only comprisearchitectural features of the first plurality of architectural featuresand not any architectural features of the second plurality ofarchitectural features, which also were extracted from each of the pastAA patients.

The method 700 comprises an eighth operation 716. At the eighthoperation 716, risk scores are generated for the AA patients,respectively. For example, a first risk score is generated for the firstpast AA patient, a second risk score is generated for the second past AApatient, and so forth. Each of the risk scores is generated based on acorresponding third plurality of architectural features. For example,the first risk score is generated based on the third plurality ofarchitectural features that were extracted from the digitized H&Etraining slide image of the first past AA patient (e.g., extractedbecause the third plurality of architectural features are architecturalfeatures from either the first or second pluralities of architecturalfeatures), the second risk score is generated based on the thirdplurality of architectural features that were extracted from thedigitized H&E training slide image of the second past AA patient, and soforth. Each of the risk scores is prognostic of OS of a correspondingpast AA patient. For example, the first risk score is prognostic of theOS of the first past AA patient, the second risk score is prognostic ofthe OS of the second past AA patient, and so forth.

The eighth operation 716 is substantially similar to the eighthoperation 116, except the eighth operation 716 generates a risk scorefor each of the past AA patients, whereas the eighth operation 116generates a risk score for the AA patient (also referred to as “the AAPOI”).

The method 700 comprises a ninth operation 718. At the ninth operation718, the machine learning classifier is trained based, at least in part,on the risk scores of the AA patients. In some embodiments, the machinelearning classifier is trained to generate a classification of the AApatient (the AA POI) into either a responder group or a non-respondergroup (see, e.g., the method 200 of FIG. 2 ). In some embodiments, themachine learning classifier is trained to predict whether the EC of theAA patient (the EC of the AA POI) is either an aggressive subtype of ECor a non-aggressive subtype of EC (see, e.g., the method 400 of FIG. 4). In such embodiments, the first operation 602 comprises training amachine learning classifier to predict whether the EC of the AA patient(the EC of the AA POI) is either an aggressive subtype of EC or anon-aggressive subtype of EC. In some embodiments, the machine learningclassifier may be trained to generate a risk group classification of theAA patient (the AA POI) (see, e.g., the method 100 of FIG. 1 ). In suchembodiments, the first operation 602 comprises training a machinelearning classifier to generate a risk group classification of the AApatient (the AA POI).

In some embodiments, the machine learning classifiers may be, forexample, a QDA classifier, a SVM classifier, a LDA classifier, or someother machine learning classifier. In further embodiments, the machinelearning classifier may be trained based on, at least in part, the riskscores and at least one other feature that is prognostic of the OS(e.g., stage of past AA patient's EC). Training the machine learningclassifier includes acquiring electronic data, reading from a computerfile, receiving a computer file, reading from a computer memory, orother computerized activity not practically performed in the human mind.

The method 700 comprises a tenth operation 720. At the tenth operation720, the machine learning classifier is validated on a validationdataset of digitized H&E slide images. The validation dataset comprisesa plurality of digitized H&E validation slide images. Each of theplurality of digitized H&E validation slide images demonstrates tissuefrom a uterus of a corresponding AA patient and a portion of agynecologic tumor of the corresponding AA patient. Further, each of theplurality of digitized H&E validation slide images is associated with apast AA patient that has EC.

Further, the validation dataset and the training dataset are portions ofan original dataset (e.g., a larger collection of digitized H&E slideimages). The original dataset comprises a plurality of originaldigitized H&E slide images. Each of the plurality of original digitizedH&E slide images demonstrates tissue from a uterus of a corresponding AApatient and a portion of a gynecologic tumor of the corresponding AApatient. Further, each of the plurality of original digitized H&E slideimages is associated with a past AA patient that has EC. The originaldataset is partitioned into the validation dataset and the trainingdataset. In some embodiments, the original dataset is partitioned intothe validation dataset and the training dataset by randomly placing theoriginal digitized H&E slide images into either the validation datasetor the training dataset.

The machine learning classifier is validated on the validation dataset.In some embodiments, the machine learning classifier is validated on thevalidation dataset to ensure that the machine learning classifier isadequately able to predict a response of the AA patient (the AA POI) toa treatment plan for EC (e.g., generate a classification of the AApatient (AA POI) into either the responder group or non-respondergroup). In some embodiments, the machine learning classifier isvalidated on the validation dataset to ensure that the machine learningclassifier is adequately able to predict whether the EC of the AApatient (the EC of the AA POI) is either an aggressive subtype of EC ora non-aggressive subtype of EC. In some embodiments, the machinelearning classifier is validated on the validation dataset to ensurethat the machine learning classifier is adequately able to generate arisk group classification of the AA patient (the AA POI). In someembodiments, a k-fold cross-validation is utilized to validate themachine learning classifier. In further embodiments, a 10-foldcross-validation is utilized to validate the machine learningclassifier. It will be appreciated that, in other embodiments, othercross-validation techniques may be utilized to validate the machinelearning classifier.

FIG. 8 illustrates some embodiments of a method 800 for classifyingwhether the EC of an AA POI is either an aggressive subtype of EC or anon-aggressive subtype of EC.

The method 800 comprises a first operation 802. At the first operation802, a machine learning classifier is trained to predict whether the ECof an AA POI is either an aggressive subtype of EC or a non-aggressivesubtype of EC. The first operation 802 may be substantially the same(e.g., comprise substantially similar steps/processes, items, features,etc.) as the first operation 602 described herein.

The method 800 comprises a second operation 804. At the second operation804, a digitized H&E slide image of the AA POI is accessed. Thedigitized H&E slide image of the AA POI indicates the AA POI has EC. Thedigitized H&E slide image of the AA POI demonstrates tissue from auterus of the AA POI and at least a portion of a gynecologic tumor. Thesecond operation 804 may be substantially the same as the firstoperation 102 described herein.

The method 800 comprises a third operation 806. At the third operation806, a tumor region is defined in the digitized H&E slide image of theAA POI. The tumor region comprises a plurality of AA POI cells. Thethird operation 806 may be substantially the same as the secondoperation 104 described herein.

The method 800 comprises a fourth operation 808. At the fourth operation808, a boundary for each of the plurality of AA POI cells is defined.The fourth operation 808 may be substantially the same as the thirdoperation 106 described herein.

The method 800 comprises a fifth operation 810. At the fifth operation810, each of the plurality of AA POI cells are classified as one celltype of the plurality of cell types. In some embodiments, the pluralityof cell types is the same plurality of cell types utilized in the firstoperation 802 (see, e.g., the third operation 706 of the method 700 ofFIG. 7 ). The fifth operation 810 may be substantially the same as thethird operation 706 described herein.

The method 800 comprises a sixth operation 812. At the sixth operation812, the TILs of the AA POI as classified as either AA POI stromal TILsor AA POI epithelial TILs. The sixth operation 812 may be substantiallythe same as the fifth operation 110 described herein.

The method 800 comprises a seventh operation 814. At the seventhoperation 814, one or more clusters of AA POI stromal TILs aregenerated. The seventh operation 814 may be substantially the same asthe sixth operation 112 described herein.

The method 800 comprises an eighth operation 816. At the eighthoperation 816, a third plurality of architectural features are extractedfrom the one or more clusters of AA POI stromal TILs. In someembodiments, the third plurality of architectural features is the samethird plurality of architectural features utilized in the firstoperation 802 (see, e.g., the seventh operation 714 of the method 700 ofFIG. 7 ). The eighth operation 816 may be substantially the same as theseventh operation 114 described herein.

The method 800 comprises a ninth operation 818. At the ninth operation818, a risk score for the AA POI is generated based on the thirdplurality of architectural features. The ninth operation 818 may besubstantially the same as the eighth operation 116 described herein.

The method 800 comprises a tenth operation 820. At the tenth operation820, the risk score for the AA POI is provided to the machine learningclassifier. The tenth operation 820 may be substantially the same as thefirst operation 402 described herein.

The method 800 comprises an eleventh operation 822. At the eleventhoperation 822, a classification of the EC of the AA POI into either theaggressive subtype of EC or the non-aggressive subtype of EC is receivedfrom the machine learning classifier. The eleventh operation 822 may besubstantially the same as the second operation 404 described herein.

The method 800 comprises a twelfth operation 824. At the twelfthoperation 824, the classification of the EC of the AA POI is displayed.The twelfth operation 824 may be substantially the same as the thirdoperation 406 described herein.

FIG. 9 illustrates a method 900 of some other embodiments of the methodof FIG. 7 .

The method 900 comprises a first operation 902. At the first operation902, a machine learning classifier is trained to generate aclassification of an AA patient (an AA POI) with EC into either aresponder group or a non-responder group. The first operation 902 may besubstantially the same (e.g., comprise substantially similarsteps/processes, items, features, etc.) as the first operation 602described herein. The first operation comprises a second operation 904,a third operation 906, a fourth operation 908, a fifth operation 910, asixth operation 912, a seventh operation 914, an eighth operation 916, aninth operation 918, a tenth operation 920, and an eleventh operation922.

At the second operation 904, a training dataset of digitized H&E slideimages is accessed. The training dataset comprises a plurality ofdigitized H&E training slide images that correspond to AA patients withEC and a plurality of digitized H&E training slide images thatcorrespond to Caucasian American (CA) patients with EC. For example, theplurality of digitized H&E training slide images comprises a firstdigitized H&E training slide image corresponding to a first past AApatient, a second digitized H&E training slide image corresponding to asecond past AA patient, a third digitized H&E training slide imagecorresponding to a first past CA patient, a fourth digitized H&Etraining slide image corresponding to a second past CA patient, and soforth.

Each of the plurality of digitized H&E training slide imagesdemonstrates tissue from a uterus of a corresponding patient and aportion of a gynecologic tumor of the corresponding patient. Further,each of the plurality of digitized H&E training slide images isassociated with a past patient that has EC. The second operation 904 maybe substantially the same (e.g., comprise substantially similarsteps/processes, items, features, etc.) as the first operation 702described herein.

At the third operation 906, a tumor region in each of the digitized H&Eslides is defined. Each tumor region comprises a plurality of individualcells of a corresponding past patient with EC. For example, the tumorregion of the first digitized H&E training slide image comprises aplurality of individual cells of the first past AA patient, the tumorregion of the second digitized H&E training slide image comprises aplurality of individual cells of the second past AA patient, the tumorregion of the third digitized H&E training slide image comprises aplurality of individual cells of the first past CA patient, the tumorregion of the fourth digitized H&E training slide image comprises aplurality of individual cells of the second past CA patient, and soforth. In some embodiments, the plurality of individual cells of thepast AA patients are referred to as the plurality of AA cells, and theplurality of individual cells of the past CA patients are referred to asthe plurality of CA cells. The third operation 906 is substantially thesame as the second operation 704 described herein.

At the fourth operation 908, each of the individual cells are classifiedas one cell type of a plurality of cell types. The plurality of celltypes comprises tumor-infiltrating lymphocytes (TILs), non-lymphocytecells, and cancer cells. The fourth operation 908 is substantially thesame as the third operation 706.

At the fifth operation 910, for each of the corresponding plurality ofindividual cells, one or more clusters of stromal TILs and one or moreclusters of epithelial TILs are generated. For example, one or moreclusters of AA stromal TILs and one or more clusters of AA epithelialTILs are generated for the plurality of AA cells of the first past AApatient, one or more clusters of AA stromal TILs and one or moreclusters of AA epithelial TILs are generated for the plurality of AAcells of the second past AA patient, one or more clusters of CA stromalTILs and one or more clusters of CA epithelial TILs are generated forthe plurality of CA cells of the first past CA patient, one or moreclusters of CA stromal TILs and one or more clusters of CA epithelialTILs are generated for the plurality of CA cells of the second past CApatient, and so forth. The fifth operation 910 is substantially the sameas the fourth operation 708. In some embodiments, the method 900 mayalso comprises defining a boundary for each of the AA cells of each ofthe corresponding pluralities of AA cells and each of the CA cells ofeach of the corresponding pluralities of CA cells (see, e.g., thirdoperation 106 of the method 100 of FIG. 1 ).

At the sixth operation 912, for each of the corresponding plurality ofindividual cells, a first plurality of architectural features areextracted from the one or more clusters of stromal TILs. In other words,for each of the corresponding plurality of individual cells, a firstplurality of architectural features are extracted from the digitized H&Etraining slide images, where the first plurality of architecturalfeatures are at least partially based on corresponding ones of the oneor more clusters of stromal TILs.

For example, a first plurality of architectural features are extractedfrom the one or more clusters of stromal TILs (AA stromal TILs) thatwere generated for the first past AA patient, a first plurality ofarchitectural features are extracted from the one or more clusters ofstromal TILs (AA stromal TILs) that were generated for the second pastAA patient, a first plurality of architectural features are extractedfrom the one or more clusters of stromal TILs (CA stromal TILs) thatwere generated for the first past CA patient, a first plurality ofarchitectural features are extracted from the one or more clusters ofstromal TILs (CA stromal TILs) that were generated for the second pastCA patient, and so forth. In some embodiments, the first plurality ofarchitectural features are collectively referred to as (e.g., each ofthe first plurality of architectural features are collectively referredto as) first pluralities of architectural features. The sixth operation912 is substantially the same as the fifth operation 710.

At the seventh operation 914, for each of the corresponding plurality ofindividual cells, a second plurality of architectural features areextracted from the one or more clusters of epithelial TILs. In otherwords, for each of the corresponding plurality of AA cells, a secondplurality of architectural features are extracted from the digitized H&Etraining slide images, where the second plurality of architecturalfeatures are at least partially based on corresponding ones of the oneor more clusters of epithelial TILs.

For example, a second plurality of architectural features are extractedfrom the one or more clusters of epithelial TILs (AA epithelial TILs)that were generated for the first past AA patient, a second plurality ofarchitectural features are extracted from the one or more clusters ofepithelial TILs (AA epithelial TILs) that were generated for the secondpast AA patient, a second plurality of architectural features areextracted from the one or more clusters of epithelial TILs (CAepithelial TILs) that were generated for the first past CA patient, asecond plurality of architectural features are extracted from the one ormore clusters of epithelial TILs (CA epithelial TILs) that weregenerated for the second past CA patient, and so forth. In someembodiments, the second plurality of architectural features arecollectively referred to as second pluralities of architecturalfeatures. The seventh operation 914 is substantially the same as thesixth operation 712.

In some embodiments, for each of the corresponding plurality ofindividual cells, other pluralities of architectural features areextracted from the digitized H&E training slide images. For example, insome embodiments, a fourth plurality of architectural features areextracted from one or more clusters of cancer cells (and/ornon-lymphocyte cells) that were generated for the first past AA patient,a fourth plurality of architectural features are extracted from one ormore clusters of cancer cells (and/or non-lymphocyte cells) that weregenerated for the second past AA patient, a fourth plurality ofarchitectural features are extracted from one or more clusters of cancercells (and/or non-lymphocyte cells) that were generated for the firstpast CA patient, a fourth plurality of architectural features areextracted from one or more clusters of cancer cells (and/ornon-lymphocyte cells) that were generated for the second past CApatient, and so forth. In such embodiments, it will be appreciated that,for each corresponding plurality of individual cells one or moreclusters of cancer cells and/or one or more clusters of non-lymphocytecells are generated (e.g., in a substantially similar manner asgenerating the one or more clusters of stromal TILs and the one or moreclusters of epithelial TILs).

At the eighth operation 916, the first pluralities of architecturalfeatures and the second pluralities of architectural features arerefined to a third plurality of architectural features. Thearchitectural features of the third plurality of architectural featuresare more relevant to predicting OS of the AA patients than the otherarchitectural features of the first pluralities of architecturalfeatures and the second pluralities of architectural features. Since thethird plurality of architectural features are more relevant topredicting OS of the AA patients than the other architectural featuresof the first pluralities of architectural features and the secondpluralities of architectural features, the third plurality ofarchitectural features are more predicative of OS of the AA patientsthan OS of the CA patients. In other words, the third plurality ofarchitectural features are more predictive of OS in AA patients than inCA patients.

For example, each of the first pluralities of architectural features maycomprise 40 different architectural features that were extracted fromthe one or more clusters of stromal TILs of a corresponding patient(e.g., a corresponding patient from the training cohort). Likewise, eachof the second pluralities of architectural features may comprise 45different architectural features that were extracted from the one ormore clusters of epithelial TILs of a corresponding patient (e.g., acorresponding patient from the training cohort). In other words, foreach of the AA patients and each of the CA patients (training cohortpatients), 85 different architectural features may be extracted fromeach of the digitized H&E training slide images (e.g., 40 differentarchitectural features from clusters of stromal TILs and 45 differentarchitectural features from clusters of epithelial TILs). Thearchitectural features of the third plurality of architectural featuresare more relevant to OS of the AA patients than the other architecturalfeatures of the first pluralities of architectural features and thesecond pluralities of architectural features.

A feature selection process determines which architectural features ofthe patients are more relevant to OS of the AA patients than the otherarchitectural features of the first pluralities of architecturalfeatures and the second pluralities of architectural features. In someembodiments, the feature selection process may be, for example, LASSO,LASSO Cox regression, MCRM mRMR, best subsets selection, correlationfeature selection, or the like. In further embodiments, the featureselection is MCRM. In embodiments in which the feature selection isMCRM, the third plurality of architectural features may be more relevantthan another, different refined plurality of architectural features ofthe second and third pluralities of architectural features which wereselected by a different feature selection process (e.g., best subsets).In further embodiments, refining the first pluralities of architecturalfeatures and the second pluralities of architectural features to thethird plurality of architectural features includes acquiring electronicdata, reading from a computer file, receiving a computer file, readingfrom a computer memory, or other computerized activity not practicallyperformed in the human mind.

In some embodiments, the third plurality of architectural featurescomprises architectural features from only the first plurality ofarchitectural features. In other words, each of the architecturalfeatures of the third plurality of architectural features corresponds toonly one of the architectural features of the first plurality ofarchitectural features. More specifically, each of the architecturalfeatures of the third plurality of architectural features corresponds toan architectural feature that was extracted from the one or moreclusters of stromal TILs. For example, the first plurality ofarchitectural features is extracted from each of the past patients. Eachof the architectural features of the first plurality of architecturalfeatures are based on the one or more clusters of stromal TILs of acorresponding past patient. The architectural features of the thirdplurality of architectural features may only comprise architecturalfeatures of the first plurality of architectural features and not anyarchitectural features of the second plurality of architecturalfeatures, which also were extracted from each of the past patients.

In some embodiments, a fifth plurality of architectural features, whichare refined from the first and second pluralities of architecturalfeatures, are different than the third plurality of architecturalfeatures. In further embodiments, the fifth plurality of architecturalfeatures are more relevant to OS of the CA patients than the thirdplurality of architectural features. In further embodiments, the thirdplurality of architectural features are more relevant to the OS of theAA patients than the fifth plurality of architectural features. In yetfurther embodiments, none of the architectural features of the thirdplurality of architectural features are in the fifth plurality ofarchitectural features. In other embodiments, one or more, but less thanall, of the architectural features of the third plurality ofarchitectural features are in the fifth plurality of architecturalfeatures.

In some embodiments, the third plurality of architectural featurescomprises at least four architectural features of the one or moreclusters of stromal TILs. In some embodiments, the third plurality ofarchitectural features consists of four architectural features of theone or more clusters of stromal TILs. In some embodiments, the fifthplurality of architectural features comprises seven architecturalfeatures of the one or more clusters of epithelial TILs. In otherembodiments, the fifth plurality of architectural features comprises sixarchitectural features of the one or more clusters of epithelial TILs.The eighth operation 916 is substantially the same as the seventhoperation 714.

At the ninth operation 918 risk scores are generated for the AApatients, respectively. For example, a first risk score is generated forthe first past AA patient, a second risk score is generated for thesecond past AA patient, a third risk score is generated for the firstpast CA patient, a fourth risk score is generated for the second past CApatient, and so forth. Each of the risk scores is generated based on acorresponding third plurality of architectural features. The ninthoperation 918 is substantially similar to the eighth operation 716.

At the tenth operation 920, the machine learning classifier is trainedbased, at least in part, on the risk scores of the patients. In someembodiments, the machine learning classifier is trained to generate aclassification of the AA patient (the AA POI) into either a respondergroup or a non-responder group (see, e.g., the method 200 of FIG. 2 ).In some embodiments, the machine learning classifier is trained topredict whether the EC of the AA patient (the EC of the AA POI) iseither an aggressive subtype of EC or a non-aggressive subtype of EC(see, e.g., the method 400 of FIG. 4 ). In such embodiments, the firstoperation 902 comprises training a machine learning classifier topredict whether the EC of the AA patient (the EC of the AA POI) iseither an aggressive subtype of EC or a non-aggressive subtype of EC. Insome embodiments, the machine learning classifier may be trained togenerate a risk group classification of the AA patient (the AA POI)(see, e.g., the method 100 of FIG. 1 ). In such embodiments, the firstoperation 902 comprises training a machine learning classifier togenerate a risk group classification of the AA patient (the AA POI). Thetenth operation 920 is substantially similar to the ninth operation 718.

At the eleventh operation 922, the machine learning classifier isvalidated on a validation dataset of digitized H&E slide images. Thevalidation dataset comprises a plurality of digitized H&E validationslide images. Each of the plurality of digitized H&E validation slideimages demonstrates tissue from a uterus of a corresponding patient anda portion of a gynecologic tumor of the corresponding patient. Further,each of the plurality of digitized H&E validation slide images isassociated with a past patient that has EC.

Further, the validation dataset and the training dataset are portions ofan original dataset (e.g., a larger collection of digitized H&E slideimages). The original dataset comprises a plurality of originaldigitized H&E slide images. Each of the plurality of original digitizedH&E slide images demonstrates tissue from a uterus of a correspondingpatient and a portion of a gynecologic tumor of the correspondingpatient (both CA patients and AA patients). Further, each of theplurality of original digitized H&E slide images is associated with apast patient that has EC. The original dataset is partitioned into thevalidation dataset and the training dataset.

In some embodiments, the original dataset is partitioned into thevalidation dataset and the training dataset by randomly placing theoriginal digitized H&E slide images into either the validation datasetor the training dataset. In some embodiments, the original dataset ispartitioned into the validation dataset and the training dataset whilemaintaining population balance between CA patients and AA patients.

The machine learning classifier is validated on the validation dataset.In some embodiments, the machine learning classifier is validated on thevalidation dataset to ensure that the machine learning classifier isadequately able to predict a response of the AA patient (the AA POI) toa treatment plan for EC (e.g., generate a classification of the AApatient (AA POI) into either the responder group or non-respondergroup). In some embodiments, the machine learning classifier isvalidated on the validation dataset to ensure that the machine learningclassifier is adequately able to predict whether the EC of the AApatient (the EC of the AA POI) is either an aggressive subtype of EC ora non-aggressive subtype of EC. In some embodiments, the machinelearning classifier is validated on the validation dataset to ensurethat the machine learning classifier is adequately able to generate arisk group classification of the AA patient (the AA POI). In someembodiments, a k-fold cross-validation is utilized to validate themachine learning classifier. In further embodiments, a 10-foldcross-validation is utilized to validate the machine learningclassifier. It will be appreciated that, in other embodiments, othercross-validation techniques may be utilized to validate the machinelearning classifier. The eleventh operation 922 is substantially similarto the tenth operation 720.

Example Use Case 1

An example embodiment includes training a machine learning classifier(e.g., prognostic classifier) to predict OS of an AA patient (e.g.,generate a risk group classification for the AA POI).

Methods

In this example, digitized H&E slide images from 445 post-surgeryendometrial cancer (EC) patients were chosen for this study. Thedigitized H&E slide images from the 445 post-surgery EC were chosen fromThe Cancer Genome Atlas (TCGA). The 445 post-surgery EC had furtherchemotherapy and/or radiotherapy for their EC. Further, the 445post-surgery EC had their races reported as either African American (AA)or Caucasian American (CA).

The digitized H&E slide images from the 445 post-surgery EC define adataset. The dataset was divided into a discovery set (D1, n=300) and avalidation set (D2, n=145), while ensuring population balance betweenthe two splits (D1(AA)=65, D1(CA)=235, D2(AA)=37, D2(CA)=108).

FIG. 10 illustrates a graphical representation 1000 of the criteria ofthe dataset for Example Use Case 1. More specifically, the graphicalrepresentation 1000 of FIG. 10 illustrates the criteria for includingpatients in the Example Use Case 1, the distribution of racial groups inthe dataset, and the discovery/validation split (training/validationcohort) for the dataset. It will be appreciated that the graphicalrepresentation 1000 of FIG. 10 may be an embodiment of the trainingdataset of the first operation 902.

A machine learning approach was employed to identify tumor regions, andtumor-associated stroma on the diagnostic slides and then used toautomatically identify TILs within these compartments. Graph networktheory based computational algorithms were used to capture 85quantitative descriptors of the architectural patterns of intratumoraland stromal TILs. A multivariable Cox regression model (MCRM) was usedto create population specific-prognostic models (M_(AA), M_(CA)) and apopulation-agnostic model (M_(AA+CA)) to predict OS. All 3 models wereevaluated on D2(AA), D2(CA), and D2(AA+CA).

FIG. 11 illustrates a graphical representation 1100 of some embodimentsfor quantifying TIL arrangements for Example Use Case 1 (e.g., clustersof stromal TILs, clusters of cancer cells, clusters of intratumoralTILs, etc.). The graphical representation 1100 of FIG. 11 illustrates afirst image 1102 a that illustrates an enlarged area (e.g., enlarged at40X) of one of the digitized H&E slide images. The graphicalrepresentation 1100 of FIG. 11 illustrates a second image 1102 b thatillustrates cell boundary segmentation and classification intotumor-infiltrating lymphocytes (yellow) and cancer cells (cyan). Thegraphical representation 1100 of FIG. 11 illustrates a third image 1102c that illustrates overlaid clusters of proximal cells constructed bygraph theory. The graphical representation 1100 of FIG. 11 illustrates afourth image 1102 d that illustrates arranging clusters into subgraphsof TILs (red) and cancer cell foci (green) that may interact in thetumor microenvironment. The graphical representation 1100 of FIG. 11illustrates a fifth image 1102 e that illustrates a convex hull of cellclusters and calculating of the overlapped area. It will be appreciatedthat the graphical representation 1100 of FIG. 11 may be an embodimentof one or more of the operations of the method 900 of FIG. 9 .

FIG. 12 illustrates various plots associated with survival analysisresults for the population-agnostic model (M_(AA+CA)) of the Example UseCase 1. More specifically, FIG. 12 illustrates a first, second, andthird Kaplan-Meier survival curves 1202 a-1202 c for thepopulation-agnostic model (M_(AA+CA)) of the Example Use Case 1. Thefirst Kaplan-Meier survival curve 1202 a illustrates a survival analysisof the population-agnostic model (M_(AA+CA)) on the AA+CA cohort. Thesecond Kaplan-Meier survival curve 1202 b illustrates a survivalanalysis of the population-agnostic model (M_(AA+CA)) on the AA cohort.The third Kaplan-Meier survival curve 1202 c illustrates a survivalanalysis of the population-agnostic model (M_(AA+CA)) on the CA cohort.As shown in FIG. 12 , the population-agnostic model (M_(AA+CA)) waspredicative of OS on the CA cohort (and the AA+CA cohort), but notprognostic on the AA cohort. It will be appreciated that the variousplots of FIG. 12 may be representative of some embodiments of the method900 of FIG. 9 .

FIG. 13 illustrates digitized H&E slide images of a long-term patientand digitized H&E slide images of a short-term patient of the ExampleUse Case 1. More specifically, FIG. 13 illustrates a first image 1302 a,a second image 1302 b, and a third image 1302 c that are associated withthe long-term patient, and a fourth image 1302 d, a fifth image 1302 e,and a sixth image 1302 f that are associated with the short-termpatient.

The first image 1302 a illustrates an area (e.g., enlarged area) of adigitized H&E slide image of the long-term patient (e.g., a patient thatdied on or after a threshold date) of the dataset. The second image 1302b illustrates cell boundary segmentation and classification ofindividual cells of the digitized H&E slide image of the long-termpatient. The third image 1302 c illustrates the formation of clusters ofthe individual cells of the digitized H&E slide image of the long-termpatient.

The fourth image 1302 d illustrates an area (e.g., enlarged area) of adigitized H&E slide image of the short-term patient (e.g., a patientthat died before the threshold date) of the dataset. The fifth image1302 e illustrates cell boundary segmentation and classification ofindividual cells of the digitized H&E slide image of the short-termpatient. The sixth image 1302 f illustrates the formation of clusters ofthe individual cells of the digitized H&E slide image of the short-termpatient.

In FIG. 13 , cyan illustrates stromal TILs (and clusters), blueillustrates non-TIL cells (and clusters), orange illustrates epithelialTILs (and clusters), and green illustrates cancer cells (and clusters).As shown in FIG. 13 , the clusters of cells of the long-term patient andthe short-term patient are significantly different. It will beappreciated that the various images of FIG. 13 may be representative ofsome embodiments of the method 900 of FIG. 9 .

FIG. 14 illustrates digitized H&E slide images of a long-term survivingAA patient and digitized H&E slide images of a short-term surviving AApatient for Example Use Case 1. More specifically, FIG. 14 illustrates afirst image 1402 a, a second image 1402 b, and a third image 1402 c thatare associated with the long-term surviving AA patient, and a fourthimage 1402 d, a fifth image 1402 e, and a sixth image 1402 f that areassociated with the short-term surviving AA patient.

The first image 1402 a illustrates an area (e.g., enlarged area) of adigitized H&E slide image of the long-term surviving AA patient. Thesecond image 1402 b illustrates cell boundary segmentation andclassification of individual cells of the digitized H&E slide image ofthe long-term surviving AA patient. The third image 1402 c illustratesthe formation of clusters of the individual cells of the digitized H&Eslide image of the long-term surviving AA patient.

The fourth image 1402 d illustrates an area (e.g., enlarged area) of adigitized H&E slide image of a short-term surviving AA patient. Thefifth image 1402 e illustrates cell boundary segmentation andclassification of individual cells of the digitized H&E slide image ofthe short-term surviving AA patient. The sixth image 1402 f illustratesthe formation of clusters of the individual cells of the digitized H&Eslide image of the short-term surviving AA patient.

In FIG. 14 , cyan illustrates stromal TILs (and clusters), blueillustrates stromal non-lymphocyte cells (and clusters), greenillustrates epithelial TILs (and clusters), orange illustrates cancercells (and clusters). As shown in FIG. 14 , the clusters of cells (e.g.,clusters of stromal TILs) of the long-term surviving AA patient and theshort-term surviving AA patient are significantly different. It will beappreciated that the various images of FIG. 14 may be representative ofsome embodiments of the method 900 of FIG. 9 .

FIG. 15 illustrates various plots associated with survival analysisresults for the population-specific models ((M_(AA)) and (M_(CA))) ofthe Example Use Case 1. More specifically, FIG. 15 illustrates a firstKaplan-Meier survival curve 1502 a and a second Kaplan-Meier survivalcurve 1502 b for the population-specific models ((M_(AA)) and (M_(CA)))of the Example Use Case 1. The first Kaplan-Meier survival curve 1502 aillustrates a survival analysis of the AA population-specific model(M_(AA)) on the AA cohort. The second Kaplan-Meier survival curve 1502 billustrates a survival analysis of the AA population-specific model(M_(AA)) on the CA cohort. As shown in FIG. 15 , the AApopulation-specific model (M_(AA)) is prognostic on the AA cohort butnot the CA cohort. It will be appreciated that the various plots of FIG.15 may be representative of some embodiments of the method 900 of FIG. 9.

FIG. 16 illustrates digitized H&E slide images of a long-term survivingCA patient and digitized H&E slide images of a short-term surviving CApatient for Example Use Case 1. More specifically, FIG. 16 illustrates afirst image 1602 a, a second image 1602 b, and a third image 1602 c thatare associated with the long-term surviving CA patient, and a fourthimage 1602 d, a fifth image 1602 e, and a sixth image 1602 f that areassociated with the short-term surviving CA patient.

The first image 1602 a illustrates an area (e.g., enlarged area) of adigitized H&E slide image of the long-term surviving CA patient (e.g., aCA patient that died on or after a threshold date). The second image1602 b illustrates cell boundary segmentation and classification ofindividual cells of the digitized H&E slide image of the long-termsurviving CA patient. The third image 1602 c illustrates the formationof clusters of the individual cells of the digitized H&E slide image ofthe long-term surviving CA patient.

The fourth image 1602 d illustrates an area (e.g., enlarged area) of adigitized H&E slide image of a short-term surviving CA patient (e.g., aCA patient that died before the threshold date). The fifth image 1602 eillustrates cell boundary segmentation and classification of individualcells of the digitized H&E slide image of the short-term surviving CApatient. The sixth image 1602 f illustrates the formation of clusters ofthe individual cells of the digitized H&E slide image of the short-termsurviving CA patient.

In FIG. 16 , cyan illustrates stromal TILs (and clusters), blueillustrates stromal non-lymphocyte cells (and clusters), greenillustrates epithelial TILs (and clusters), orange illustrates cancercells (and clusters). As shown in FIG. 16 , the clusters of cells (e.g.,clusters of stromal TILs) of the long-term surviving CA patient and theshort-term surviving CA patient are significantly different. It will beappreciated that the various images of FIG. 16 may be representative ofsome embodiments of the method 900 of FIG. 9 .

FIG. 17 illustrates various plots associated with survival analysisresults for the population-specific models ((M_(AA)) and (M_(CA))) ofthe Example Use Case 1. More specifically, FIG. 17 illustrates a firstKaplan-Meier survival curve 1702 a and a second Kaplan-Meier survivalcurve 1702 b for the population-specific models ((M_(AA)) and (M_(CA)))of the Example Use Case 1. The first Kaplan-Meier survival curve 1702 aillustrates a survival analysis of the CA population-specific model(M_(CA)) on the AA cohort. The second Kaplan-Meier survival curve 1702 billustrates a survival analysis of the CA population-specific model(M_(CA)) on the CA cohort. As shown in FIG. 17 , the CApopulation-specific model (M_(CA)) is prognostic on the CA cohort butnot the AA cohort. It will be appreciated that the various plots of FIG.17 may be representative of some embodiments of the method 900 of FIG. 9.

Results

M_(AA) identified 4 prognostic features relating to interaction(s) ofTIL clusters with cancer nuclei in the stromal compartment and wasprognostic of OS on D2(AA) (see, Table 1), but not prognostic in D2(CA)nor D2(AA+CA). M_(CA) and M_(AA+CA) identified respectively 7 and 6prognostic features relating to interaction(s) of TIL clusters withcancer nuclei (both in the epithelial and stromal regions) and wereprognostic of OS on D2(CA) and D2(AA+CA), but not prognostic in D2(AA).

TABLE 1 M_(AA) M_(AA+CA) M_(CA) HR CI P HR CI P HR CI P D2(AA) 6.16 1.55-24.45 0.01 0.91 0.23-3.62 0.9 1.40 0.36-5.52 0.6 D2 2.12 0.94-4.770.07 3.99 1.62-9.78 0.03 2.38 1.07-5.31 0.03 D2(CA) 1.93 0.71-5.24 0.27.34  2.12-25.47 0.02 3.47 1.24-9.77 0.02

FIG. 18 illustrates a graphical representation 1800 of an overview ofthe results of Example Use Case 1.

CONCLUSION

As described herein, stromal TIL architecture are more prognostic of OSin AA women with EC, while epithelial TIL features were more prognosticin CA women.

As demonstrated by the example embodiments, various embodiments canfacilitate classifying a AA patient into either a high risk group or alow risk group based on a risk score of the AA patient. By being able toclassify the AA patient into either the high risk group or the low riskgroup based on the risk score of the AA patient, a medical practitionermay be able to easily and timely (e.g., intuitively due to the singleclassification being displayed) determine the time in which the AApatient has to live. Accordingly, the medical practitioner may be ableto accurately guide the EC treatment of the AA patient to achieve bettertreatment results (e.g., expedite alternative treatment options (e.g.,adjuvant therapy), choose a less aggressive treatment plan to reducenegative side effects, etc.). Further, the medical practitioner may beable to provide better care to the AA patient (e.g., improve patientsatisfaction and/or knowledge) by being able to better predict lifeexpectancy and provide this information to the AA patient. Embodimentsthus provide a measurable improvement over existing methods, systems,apparatus, or other devices in improving the treatment of AA patientswith EC.

In various example embodiments, method(s) discussed herein can beimplemented as computer executable instructions. Thus, in variousembodiments, a computer-readable storage device can store computerexecutable instructions that, when executed by a machine (e.g.,computer, processor), cause the machine to perform methods or operationsdescribed or claimed herein including operation(s) described inconnection with methods or operations 100, 200, 300, 400, 500, 600, 700,800, 900, or any other methods or operations described herein. Whileexecutable instructions associated with the listed methods are describedas being stored on a computer-readable storage device, it is to beappreciated that executable instructions associated with other examplemethods or operations described or claimed herein can also be stored ona computer-readable storage device. In different embodiments, theexample methods or operations described herein can be triggered indifferent ways. In one embodiment, a method or operation can betriggered manually by a user. In another example, a method or operationcan be triggered automatically.

Embodiments discussed herein related to generating the risk groupclassification of the AA patient are based on architectural featuresthat may not be perceivable by the human eye, and their computationcannot be practically performed in the human mind. A machine learningclassifier as described herein cannot be implemented in the human mindor with pencil and paper. Embodiments thus perform actions, steps,processes, or other actions that are not practically performed in thehuman mind, at least because they require a processor or circuitry toaccess digitized images stored in a computer memory and to extract orcompute features that are based on the digitized images and not onproperties of tissue or the images that are perceivable by the humaneye. Embodiments described herein can use a combined order of specificrules, elements, operations, or components that render information intoa specific format that can then be used and applied to create desiredresults more accurately, more consistently, and with greater reliabilitythan existing approaches, thereby producing the technical effect ofimproving the performance of the machine, computer, or system with whichembodiments are implemented.

FIG. 19 illustrates some embodiments of an apparatus that can facilitatethe methods described herein. For example, FIG. 19 illustrates someembodiments of an apparatus 1900 that can facilitate classifying a newAA patient into a high risk group or a low risk group based onarchitectural features extracted from a digitized H&E slide image of theAA patient, according to various embodiments discussed herein.

Apparatus 1900 may be configured to perform various techniques,operations, or methods discussed herein, for example, training a machinelearning classifier (e.g., linear discriminant analysis, quadraticdiscriminant analysis classifier, support vector machine, etc.) based ona training dataset to classify an AA patient into the high risk group orthe low risk group (e.g., generate a risk score classification of the AApatient), employing the trained machine learning classifier to generatea classification of the AA patient into either a responder ornon-responder group, and/or employing the trained machine learningclassifier to generate a classification of the EC of the AA patient aseither an aggressive subtype of EC or a non-aggressive subtype of EC.

In one embodiment, apparatus 1900 includes a processor 1902, and amemory 1904. Processor 1902 may, in various embodiments, includecircuitry such as, but not limited to, one or more single-core ormulti-core processors. Processor 1902 may include any combination ofgeneral-purpose processors and dedicated processors (e.g., graphicsprocessors, application processors, etc.). The processor(s) can becoupled with and/or can comprise memory (e.g., memory 1904) or storageand can be configured to execute instructions stored in the memory 1904or storage to enable various apparatus, applications, or operatingsystems to perform operations and/or methods discussed herein.

Memory 1904 is configured to store a digitized H&E slide image of the AApatient. In some embodiments, memory 1904 can also store a trainingdataset of digitized H&E slide images for training the machine learningclassifier (e.g., linear discriminant analysis classifier, etc.), and/ora validation dataset of digitized H&E slide image. Memory 1904 can befurther configured to store one or more clinical features (e.g., cancerstage) or other data associated with the AA patient.

Apparatus 1900 also includes an input/output (I/O) interface 1906; a setof circuits 1910; and an interface 1908 that connects the processor1902, the memory 1904, the I/O interface 1906, and the set of circuits1910. I/O interface 1906 may be configured to transfer data betweenmemory 1904, processor 1902, circuits 1910, and external devices, forexample, an imaging device (e.g., digital microscope).

The set of circuits 1910 includes an image acquisition circuit 1912, aregion processing circuit 1914, an architectural feature extractioncircuit 1916, a risk score generation circuit 1918, a classificationcircuit 1920, and a display circuit 1922.

The image acquisition circuit 1912 is configured to access the digitizedH&E slide image of the AA patient, according to embodiments and examplesdescribed. The image acquisition circuit 1912 may also be configured toaccess the digitized H&E slide images of the validation dataset and/ortraining dataset, according to embodiments and examples described.Accessing the digitized H&E slide image may include accessing thedigitized H&E slide image stored in memory 1904. In another embodimentaccessing the digitized H&E slide image may include acquiring electronicdata, reading from a computer file, receiving a computer file, readingfrom a computer memory, or other computerized activity not practicallyperformed in the human mind.

Region processing circuit 1914 is configured to define a tumor region inthe digitized H&E slide image, classify a plurality of individual cellsinto cell types, classify TILs as stromal TILs or epithelial TILs, andgenerate one or more clusters of stromal TILs, according to embodimentsand examples described.

Architectural feature extraction circuit 1916 is configured to extract aplurality of architectural features from the digitized H&E slide image,according to embodiments and examples described.

Risk score generation circuit 1918 is configured to generate a riskscore for the AA patient based on the plurality of architecturalfeatures, according to embodiments and examples described. The riskscore generation circuit 1918 may also be configured to generate riskscores for the digitized H&E slide images of the validation datasetand/or the digitized H&E slide image of the training dataset, accordingto embodiments and examples described.

Classification circuit 1920 is configured to generate a risk groupclassification of the AA patient based on, at least partially, the riskscore of the AA patient, according to embodiments and examplesdescribed. The classification circuit 1920 may also be configured togenerate a risk group classification of past EC patients (e.g., of thevalidation/training dataset), according to embodiments and examplesdescribed.

Display circuit 1922 is configured to display the risk groupclassification of the AA patient, according to embodiments and examplesdescribed. The display circuit 1922 may also be configured to displaythe classification of past EC patients, according to embodiments andexamples described.

FIG. 20 illustrates some other embodiments of the apparatus 1900 of FIG.19 . As shown in FIG. 20 , in some embodiments, the set of circuits 1910further includes a training and validating circuit 2002. The trainingand validating circuit 2002 is configured to train the classificationcircuit 1920 on a training dataset (e.g., a training cohort); andoptionally validate the classification circuit 1920 on a validationdataset (e.g., a validation cohort), according to various embodimentsdescribed herein.

FIG. 21 illustrates some embodiments of a computer in which methodsdescribed herein can operate and in which example methods, apparatus,circuits, operations, or logics may be implemented. In differentexamples, computer 2100 may be part of an AA patient OS predictionsystem or an imaging system, or may be operably coupled to an AA patientOS prediction system or an imaging system.

Computer 2100 includes a processor 2102, a memory 2104, and input/output(I/O) ports 2106 operably connected by a bus 2108. In one example,computer 2100 may include a set of logics or circuits 2110 that performoperations for or a method of classifying an AA patient into the highrisk group or the low risk group (e.g., generating a risk groupclassification of the AA patient), generating a classification of the AApatient into either a responder or non-responder group, and/orgenerating a classification of the EC of the AA patient as either anaggressive subtype of EC or a non-aggressive subtype of EC, according toembodiments and examples described. Thus, the set of circuits 2110,whether implemented in computer 2100 as hardware, firmware, software,and/or a combination thereof may provide means (e.g., hardware,firmware, circuits) for classifying an AA patient into the high riskgroup or the low risk group (e.g., generating a risk groupclassification of the AA patient), generating a classification of the AApatient into either a responder or non-responder group, and/orgenerating a classification of the EC of the AA patient as either anaggressive subtype of EC or a non-aggressive subtype of EC, according toembodiments and examples described. In different examples, the set ofcircuits 2110 may be permanently and/or removably attached to computer2100.

Processor 2102 can be a variety of various processors including dualmicroprocessor and other multi-processor architectures. Processor 2102may be configured to perform steps of methods claimed and describedherein. Memory 2104 can include volatile memory and/or non-volatilememory. A disk 2112 may be operably connected to computer 2100 via, forexample, an input/output interface 2118 (e.g., card, device) and aninput/output port 2106. Disk 2112 may include, but is not limited to,devices like a magnetic disk drive, a tape drive, a Zip drive, a flashmemory card, or a memory stick. Furthermore, disk 2112 may includeoptical drives like a CD-ROM or a digital video ROM drive (DVD ROM).Memory 2104 can store processes 2114 or data 2116, for example. Data2116 may, in one embodiment, include digitized H&E slide images,according to embodiments and examples described. Disk 2112 or memory2104 can store an operating system that controls and allocates resourcesof computer 2100.

Bus 2108 can be a single internal bus interconnect architecture or otherbus or mesh architectures. While a single bus is illustrated, it is tobe appreciated that computer 2100 may communicate with various devices,circuits, logics, and peripherals using other buses that are notillustrated (e.g., PCIE, SATA, Infiniband, IEEE 1394, USB, Ethernet).

Computer 2100 may interact with input/output devices via I/O interfaces2118 and the I/O ports 2106. Input/output devices can include, but arenot limited to, MRI systems, CT systems, a keyboard, a microphone, apointing and selection device, cameras, video cards, displays, disk2112, network devices 2120, or other devices. The I/O ports 2106 caninclude but are not limited to, serial ports, parallel ports, or USBports.

Computer 2100 may operate in a network environment and thus may beconnected to network devices 2120 via I/O interfaces 2118 or I/O ports2106. Through the network devices 2120, computer 2100 may interact witha network. Through the network, computer 2100 may be logically connectedto remote computers. The networks with which computer 2100 may interactinclude, but are not limited to, a local area network (LAN), a wide areanetwork (WAN), or other networks, including the cloud.

Examples herein can include subject matter such as an apparatus, aprocessor, a system, circuitry, a method, means for performing acts,steps, or blocks of the method, at least one machine-readable mediumincluding executable instructions that, when performed by a machine(e.g., a processor with memory, an application-specific integratedcircuit (ASIC), a field programmable gate array (FPGA), or the like)cause the machine to perform acts of the method or of an apparatus orsystem for classifying an AA patient into the high risk group or the lowrisk group (e.g., generating a risk group classification of the AApatient), generating a classification of the AA patient into either aresponder or non-responder group, and/or generating a classification ofthe EC of the AA patient as either an aggressive subtype of EC or anon-aggressive subtype of EC, according to embodiments and examplesdescribed.

In some embodiments, the present application provides a method. Themethod comprises accessing a digitized H&E slide image of an AfricanAmerican (AA) patient, wherein the digitized H&E slide image of the AApatient demonstrates one or more indicators of endometrial cancer (EC),and wherein the digitized H&E slide of the AA patient demonstratestissue from a uterus of the AA patient and at least a portion of agynecologic tumor. A tumor region is defined in the digitized H&E slideimage, wherein the tumor region comprises at least a portion of thegynecologic tumor, and wherein the tumor region comprises a plurality ofindividual cells. A boundary for each of the plurality of individualcells is defined. The plurality of individual cells are classified intocell types, wherein the cell types comprise tumor-infiltratinglymphocytes (TILs), non-lymphocyte cells, and cancer cells. The TILs areclassified as stromal TILs or epithelial TILs. A cluster of stromal TILsare generated, wherein the cluster of stromal TILs comprises a subset ofstromal TILs that are related to one another based on proximity. A firstplurality of architectural features are extracted from the digitized H&Eslide image of the AA patient, wherein each of the first plurality ofarchitectural features are at least partially based on the cluster ofstromal TILs. A risk score is generated for the AA patient based on thefirst plurality of architectural features, wherein the risk score isprognostic of overall survival (OS) of the AA patient. A risk groupclassification is generated for the AA patient, wherein generating therisk group classification comprises classifying the AA patient intoeither a high risk group or a low risk group based on the risk score,wherein the high risk group indicates the AA patient will die before athreshold date and the low risk group indicates the AA patient will dieafter or on the threshold date. The risk group classification of the AApatient is displayed.

In some embodiments, the method further comprises: providing the riskscore to a machine learning classifier that is trained to predict aresponse of the AA patient to a treatment plan for the EC; receiving,from the machine learning classifier, a classification of the AA patientinto either a responder group (RG) or a non-responder group (NRG), wherethe NRG indicates the AA patient will not respond to the treatment planand the RG indicates that the AA patient will respond to the treatmentplan; and displaying the classification of the AA patient as either inthe NRG or in the RG.

In some embodiments, the treatment plan comprises at least one ofchemotherapy and radiation.

In some embodiments, the method further comprises: extracting a secondplurality of architectural features from the digitized H&E slide imageof the AA patient, wherein each of the second plurality of architecturalfeatures are at least partially based on the cluster of stromal TILs;and selecting a subset of architectural features of the second pluralityof architectural features, wherein the subset of architectural featuresof the second plurality of architectural features are more relevant topredicting OS of AA patients with EC than the other architecturalfeatures of the second plurality of architectural features for apredefined feature selection process, and wherein the subset ofarchitectural features defines the first plurality of architecturalfeatures.

In some embodiments, selecting the subset of architectural features ofthe second plurality of architectural features comprises performing aleast absolute shrinkage and selection operator (LASSO) technique on thesecond plurality of architectural features.

In some embodiments, the first plurality of architectural features arebased only on the cluster of stromal TILs.

In some embodiments, generating the risk score for the AA patient basedon the first plurality of architectural features comprises: assigning avalue to each of the architectural features of the first plurality ofarchitectural features; assigning a weighting coefficient to each of thevalues; and combining the values and their respective weightingcoefficients linearly to generate the risk score.

In some embodiments, the cluster of stromal TILs is generated via agraph theory technique.

In some embodiments, classifying the AA patient into either the highrisk group or the low risk group comprises comparing the risk score ofthe AA patient to a threshold value.

In some embodiments, classifying the AA patient into either the highrisk group or the low risk group further comprises: classifying the AApatient into the high risk group if the risk score for the AA patient isgreater than the threshold value; and classifying the AA patient intothe low risk group if the risk score for the AA patient is less than orequal to the threshold value.

In some embodiments, the present application provides a non-transitorycomputer-readable storage device storing computer-executableinstructions that when executed cause a processor to perform operations.The operations comprise: accessing a digitized H&E slide image of an(AA) African American patient, wherein the digitized H&E slide image ofthe AA patient demonstrates one or more indicators of endometrial cancer(EC), and wherein the digitized H&E slide image of the AA patientdemonstrates tissue from a uterus of the AA patient and at least aportion of a gynecologic tumor; defining a tumor region in the digitizedH&E slide image of the AA patient, wherein the tumor region comprises atleast a portion of the gynecologic tumor, and wherein the tumor regioncomprises a plurality of individual cells; classifying the plurality ofindividual cells into cell types, wherein the cell types comprisetumor-infiltrating lymphocytes (TILs) and non-lymphocyte cells;classifying the TILs as intratumoral TILs or stromal TILs; generatingone or more clusters of stromal TILs, wherein each of the one or moreclusters of stromal TILs comprises a subset of stromal TILs that arerelated to one another based on proximity; extracting a plurality ofarchitectural features from the digitized H&E slide image of the AApatient, wherein the plurality of architectural features are at leastpartially based on the one or more clusters of stromal TILs; generatinga risk score for the AA patient based on the plurality of architecturalfeatures, wherein the risk score is prognostic of overall survival (OS)of the AA patient; providing the risk score to a machine learningclassifier that is trained to predict whether the EC of the AA patientis either an aggressive subtype of EC or a non-aggressive subtype of EC;receiving, from the machine learning classifier, a classification of theEC of the AA patient as either the aggressive subtype of EC or thenon-aggressive subtype of EC; and displaying the classification of theEC of the AA patient.

In some embodiments, each of the plurality of architectural featurescorresponds to a different architectural feature of the one or moreclusters of stromal TILs.

In some embodiments, generating the risk score comprises: assigning aplurality of values to the plurality of architectural features,respectively, wherein each of the values of the plurality of valuescorresponds to a number of times a corresponding architectural featureof the plurality of architectural features is present in the digitizedH&E slide image of the AA patient; and combining, linearly, theplurality of values with a plurality of weighting coefficients, whereinthe plurality of weighting coefficients are attached to the plurality ofvalues, respectively.

In some embodiments, the plurality of architectural features comprisesat least four architectural features.

In some embodiments, the plurality of architectural features are basedonly on the one or more clusters of stromal TILs.

In some embodiments, the plurality of architectural features consists offour architectural features.

In some embodiments, generating the one or more clusters of stromal TILscomprises grouping the stromal TILs into corresponding clusters of theone or more clusters of stromal TILs based on a distance in which thestromal TILs are spaced from one another, wherein each stromal TIL of agiven cluster of the one or more clusters of stromal TILs is spaced froma neighboring stromal TIL of the given cluster by less than a thresholddistance.

In some embodiments, the present application provides a non-transitorycomputer-readable storage device storing computer-executableinstructions that when executed cause a processor to perform operations.The operations comprising: accessing a training dataset of digitized H&Eslide images, wherein the training dataset of digitized H&E slide imagescomprises a plurality of digitized H&E training slide images of AApatients, wherein each of the digitized H&E training slide imagesdemonstrates tissue from a uterus of a corresponding AA patient and aportion of a gynecologic tumor of the corresponding AA patient; definingan AA tumor region for each of the digitized H&E training slide images,wherein each of the AA tumor regions comprises a corresponding pluralityof AA cells; defining a boundary for each of the AA cells of each of thecorresponding pluralities of AA cells; classifying each of the AA cellsas one cell type of a plurality of cell types, wherein the plurality ofcell types comprises tumor-infiltrating lymphocytes (TILs),non-lymphocyte cells, and cancer cells; for each corresponding pluralityof AA cells, generating one or more clusters of AA stromal TILs and oneor more clusters of AA epithelial TILs; for each of the correspondingplurality of AA cells, extracting a first plurality of architecturalfeatures from the one or more clusters of AA stromal TILs; for each ofthe corresponding plurality of AA cells, extracting a second pluralityof architectural features from the one or more clusters of AA epithelialTILs; refining the first pluralities of architectural features and thesecond pluralities of architectural features to a third plurality ofarchitectural features, wherein the architectural features of the thirdplurality of architectural features are more relevant to predictingoverall survival (OS) of the AA patients than the other architecturalfeatures of the first pluralities of architectural features and thesecond pluralities of architectural features; generating risk scores forthe AA patients, respectively, wherein each of the risk scores for theAA patients is generated based on the third plurality of architecturalfeatures of a corresponding digitized H&E slide image of an AA patientof the plurality of digitized H&E training slide images; and training amachine learning classifier based on the risk scores for the AApatients, wherein the machine learning classifier is trained to predicta difference between an aggressive subtype of endometrial cancer (EC)and a non-aggressive subtype of EC.

In some embodiments, the third plurality of architectural featurescomprises architectural features from only the first plurality ofarchitectural features.

In some embodiments, the operation further comprise: accessing adigitized H&E slide image of an AA patient of interest (POI), whereinthe digitized H&E slide image of the AA POI indicates the AA POI has EC,and wherein the digitized H&E slide image of the AA POI demonstratestissue from a uterus of the AA POI and at least a portion of agynecologic tumor; defining a tumor region in the digitized H&E slideimage of the AA POI, wherein the tumor region comprises a plurality ofAA POI cells; defining a boundary for each of the plurality of AA POIcells; classifying each of the plurality of AA POI cells as one celltype of the plurality of cell types; generating one or more clusters ofAA POI stromal TILs; extracting the third plurality of architecturalfeatures from the one or more clusters of AA POI stromal TILs;generating a risk score for the AA POI based on the third plurality ofarchitectural features that were extracted from the one or more clustersof AA POI stromal TILs; providing the risk score for the AA POI to themachine learning classifier; receiving, from the machine learningclassifier, a classification of the EC of the AA POI into either theaggressive subtype of EC or the non-aggressive subtype of EC; anddisplaying the classification of the EC of the AA POI.

Examples herein can include subject matter such as an apparatus,including an NSCLC immunotherapy response prediction apparatus orsystem, a digital whole slide scanner, a CT system, an MRI system, apersonalized medicine system, a CADx system, a processor, a system,circuitry, a method, means for performing acts, steps, or blocks of themethod, at least one machine-readable medium including executableinstructions that, when performed by a machine (e.g., a processor withmemory, an application-specific integrated circuit (ASIC), a fieldprogrammable gate array (FPGA), or the like) cause the machine toperform acts of the method or of an apparatus or system for predictingNSCLC immunotherapy response, according to embodiments and examplesdescribed.

References to “one embodiment”, “an embodiment”, “one example”, and “anexample” indicate that the embodiment(s) or example(s) so described mayinclude a particular feature, structure, characteristic, property,element, or limitation, but that not every embodiment or examplenecessarily includes that particular feature, structure, characteristic,property, element or limitation. Furthermore, repeated use of the phrase“in one embodiment” does not necessarily refer to the same embodiment,though it may.

“Computer-readable storage device”, as used herein, refers to a devicethat stores instructions or data. “Computer-readable storage device”does not refer to propagated signals. A computer-readable storage devicemay take forms, including, but not limited to, non-volatile media, andvolatile media. Non-volatile media may include, for example, opticaldisks, magnetic disks, tapes, and other media. Volatile media mayinclude, for example, semiconductor memories, dynamic memory, and othermedia. Common forms of a computer-readable storage device may include,but are not limited to, a floppy disk, a flexible disk, a hard disk, amagnetic tape, other magnetic medium, an application specific integratedcircuit (ASIC), a compact disk (CD), other optical medium, a randomaccess memory (RAM), a read only memory (ROM), a memory chip or card, amemory stick, and other media from which a computer, a processor orother electronic device can read.

“Circuit”, as used herein, includes but is not limited to hardware,firmware, software in execution on a machine, or combinations of each toperform a function(s) or an action(s), or to cause a function or actionfrom another logic, method, or system. A circuit may include a softwarecontrolled microprocessor, a discrete logic (e.g., ASIC), an analogcircuit, a digital circuit, a programmed logic device, a memory devicecontaining instructions, and other physical devices. A circuit mayinclude one or more gates, combinations of gates, or other circuitcomponents. Where multiple logical circuits are described, it may bepossible to incorporate the multiple logical circuits into one physicalcircuit. Similarly, where a single logical circuit is described, it maybe possible to distribute that single logical circuit between multiplephysical circuits.

To the extent that the term “includes” or “including” is employed in thedetailed description or the claims, it is intended to be inclusive in amanner similar to the term “comprising” as that term is interpreted whenemployed as a transitional word in a claim.

Throughout this specification and the claims that follow, unless thecontext requires otherwise, the words ‘comprise’ and ‘include’ andvariations such as ‘comprising’ and ‘including’ will be understood to beterms of inclusion and not exclusion. For example, when such terms areused to refer to a stated integer or group of integers, such terms donot imply the exclusion of any other integer or group of integers.

To the extent that the term “or” is employed in the detailed descriptionor claims (e.g., A or B) it is intended to mean “A or B or both”. Whenthe applicants intend to indicate “only A or B but not both” then theterm “only A or B but not both” will be employed. Thus, use of the term“or” herein is the inclusive, and not the exclusive use. See, Bryan A.Garner, A Dictionary of Modern Legal Usage 624 (2 d. Ed. 1995).

While example systems, methods, and other embodiments have beenillustrated by describing examples, and while the examples have beendescribed in considerable detail, it is not the intention of theapplicants to restrict or in any way limit the scope of the appendedclaims to such detail. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing the systems, methods, and other embodiments described herein.Therefore, the invention is not limited to the specific details, therepresentative apparatus, and illustrative examples shown and described.Thus, this application is intended to embrace alterations,modifications, and variations that fall within the scope of the appendedclaims.

What is claimed is:
 1. A method, comprising: accessing a digitized H&Eslide image of an African American (AA) patient, wherein the digitizedH&E slide image of the AA patient demonstrates one or more indicators ofendometrial cancer (EC), and wherein the digitized H&E slide of the AApatient demonstrates tissue from a uterus of the AA patient and at leasta portion of a gynecologic tumor; defining a tumor region in thedigitized H&E slide image, wherein the tumor region comprises at least aportion of the gynecologic tumor, and wherein the tumor region comprisesa plurality of individual cells; defining a boundary for each of theplurality of individual cells; classifying the plurality of individualcells into cell types, wherein the cell types comprisetumor-infiltrating lymphocytes (TILs), non-lymphocyte cells, and cancercells; classifying the TILs as stromal TILs or epithelial TILs;generating a cluster of stromal TILs, wherein the cluster of stromalTILs comprises a subset of stromal TILs that are related to one anotherbased on proximity; extracting a first plurality of architecturalfeatures from the digitized H&E slide image of the AA patient, whereineach of the first plurality of architectural features are at leastpartially based on the cluster of stromal TILs; generating a risk scorefor the AA patient based on the first plurality of architecturalfeatures, wherein the risk score is prognostic of overall survival (OS)of the AA patient; generating a risk group classification for the AApatient, wherein generating the risk group classification comprisesclassifying the AA patient into either a high risk group or a low riskgroup based on the risk score, wherein the high risk group indicates aprobability that the AA patient will die within a date range is greaterthan a threshold probability, and wherein the low risk group indicatesthe probability that the AA patient will die within the date range isless than or equal to the threshold probability; and displaying the riskgroup classification of the AA patient.
 2. The method of claim 1,further comprising: providing the risk score to a machine learningclassifier that is trained to predict a response of the AA patient to atreatment plan for the EC; receiving, from the machine learningclassifier, a classification of the AA patient into either a respondergroup (RG) or a non-responder group (NRG), where the NRG indicates theAA patient will not respond to the treatment plan and the RG indicatesthat the AA patient will respond to the treatment plan; and displayingthe classification of the AA patient as either in the NRG or in the RG.3. The method of claim 2, wherein the treatment plan comprises at leastone of chemotherapy and radiation.
 4. The method of claim 1, furthercomprising: extracting a second plurality of architectural features fromthe digitized H&E slide image of the AA patient, wherein each of thesecond plurality of architectural features are at least partially basedon the cluster of stromal TILs; and selecting a subset of architecturalfeatures of the second plurality of architectural features, wherein thesubset of architectural features of the second plurality ofarchitectural features are more relevant to predicting OS of AA patientswith EC than the other architectural features of the second plurality ofarchitectural features for a predefined feature selection process, andwherein the subset of architectural features defines the first pluralityof architectural features.
 5. The method of claim 4, wherein selectingthe subset of architectural features of the second plurality ofarchitectural features comprises: performing a least absolute shrinkageand selection operator (LASSO) technique on the second plurality ofarchitectural features.
 6. The method of claim 4, wherein the firstplurality of architectural features are based only on the cluster ofstromal TILs.
 7. The method of claim 1, wherein generating the riskscore for the AA patient based on the first plurality of architecturalfeatures comprises: assigning a value to each of the architecturalfeatures of the first plurality of architectural features; assigning aweighting coefficient to each of the values; and combining the valuesand their respective weighting coefficients linearly to generate therisk score.
 8. The method of claim 1, wherein the cluster of stromalTILs is generated via a graph theory technique.
 9. The method of claim1, wherein classifying the AA patient into either the high risk group orthe low risk group comprises comparing the risk score of the AA patientto a threshold value.
 10. The method of claim 9, wherein classifying theAA patient into either the high risk group or the low risk group furthercomprises: classifying the AA patient into the high risk group if therisk score for the AA patient is greater than the threshold value; andclassifying the AA patient into the low risk group if the risk score forthe AA patient is less than or equal to the threshold value.
 11. Anon-transitory computer-readable storage device storingcomputer-executable instructions that when executed cause a processor toperform operations, the operations comprising: accessing a digitized H&Eslide image of an (AA) African American patient, wherein the digitizedH&E slide image of the AA patient demonstrates one or more indicators ofendometrial cancer (EC), and wherein the digitized H&E slide image ofthe AA patient demonstrates tissue from a uterus of the AA patient andat least a portion of a gynecologic tumor; defining a tumor region inthe digitized H&E slide image of the AA patient, wherein the tumorregion comprises at least a portion of the gynecologic tumor, andwherein the tumor region comprises a plurality of individual cells;classifying the plurality of individual cells into cell types, whereinthe cell types comprise tumor-infiltrating lymphocytes (TILs) andnon-lymphocyte cells; classifying the TILs as intratumoral TILs orstromal TILs; generating one or more clusters of stromal TILs, whereineach of the one or more clusters of stromal TILs comprises a subset ofstromal TILs that are related to one another based on proximity;extracting a plurality of architectural features from the digitized H&Eslide image of the AA patient, wherein the plurality of architecturalfeatures are at least partially based on the one or more clusters ofstromal TILs; generating a risk score for the AA patient based on theplurality of architectural features, wherein the risk score isprognostic of overall survival (OS) of the AA patient; providing therisk score to a machine learning classifier that is trained to predictwhether the EC of the AA patient is either an aggressive subtype of ECor a non-aggressive subtype of EC; receiving, from the machine learningclassifier, a classification of the EC of the AA patient as either theaggressive subtype of EC or the non-aggressive subtype of EC; anddisplaying the classification of the EC of the AA patient.
 12. Thenon-transitory computer-readable storage device of claim 11, wherein:each of the plurality of architectural features corresponds to adifferent architectural feature of the one or more clusters of stromalTILs.
 13. The non-transitory computer-readable storage device of claim12, wherein generating the risk score comprises: assigning a pluralityof values to the plurality of architectural features, respectively,wherein each of the values of the plurality of values corresponds to anumber of times a corresponding architectural feature of the pluralityof architectural features is present in the digitized H&E slide image ofthe AA patient; and combining, linearly, the plurality of values with aplurality of weighting coefficients, wherein the plurality of weightingcoefficients are attached to the plurality of values, respectively. 14.The non-transitory computer-readable storage device of claim 13, whereinthe plurality of architectural features comprises at least fourarchitectural features.
 15. The non-transitory computer-readable storagedevice of claim 14, wherein the plurality of architectural features arebased only on the one or more clusters of stromal TILs.
 16. Thenon-transitory computer-readable storage device of claim 15, wherein theplurality of architectural features consists of four architecturalfeatures.
 17. The non-transitory computer-readable storage device ofclaim 11, wherein generating the one or more clusters of stromal TILscomprises: grouping the stromal TILs into corresponding clusters of theone or more clusters of stromal TILs based on a distance in which thestromal TILs are spaced from one another, wherein each stromal TIL of agiven cluster of the one or more clusters of stromal TILs is spaced froma neighboring stromal TIL of the given cluster by less than a thresholddistance.
 18. A non-transitory computer-readable storage device storingcomputer-executable instructions that when executed cause a processor toperform operations, the operations comprising: accessing a trainingdataset of digitized H&E slide images, wherein the training dataset ofdigitized H&E slide images comprises a plurality of digitized H&Etraining slide images of AA patients, wherein each of the digitized H&Etraining slide images demonstrates tissue from a uterus of acorresponding AA patient and a portion of a gynecologic tumor of thecorresponding AA patient; defining an AA tumor region for each of thedigitized H&E training slide images, wherein each of the AA tumorregions comprises a corresponding plurality of AA cells; defining aboundary for each of the AA cells of each of the correspondingpluralities of AA cells; classifying each of the AA cells as one celltype of a plurality of cell types, wherein the plurality of cell typescomprises tumor-infiltrating lymphocytes (TILs), non-lymphocyte cells,and cancer cells; for each corresponding plurality of AA cells,generating one or more clusters of AA stromal TILs and one or moreclusters of AA epithelial TILs; for each of the corresponding pluralityof AA cells, extracting a first plurality of architectural features fromthe one or more clusters of AA stromal TILs; for each of thecorresponding plurality of AA cells, extracting a second plurality ofarchitectural features from the one or more clusters of AA epithelialTILs; refining the first pluralities of architectural features and thesecond pluralities of architectural features to a third plurality ofarchitectural features, wherein the architectural features of the thirdplurality of architectural features are more relevant to predictingoverall survival (OS) of the AA patients than the other architecturalfeatures of the first pluralities of architectural features and thesecond pluralities of architectural features; generating risk scores forthe AA patients, respectively, wherein each of the risk scores for theAA patients is generated based on the third plurality of architecturalfeatures of a corresponding digitized H&E slide image of an AA patientof the plurality of digitized H&E training slide images; and training amachine learning classifier based on the risk scores for the AApatients, wherein the machine learning classifier is trained to predicta difference between an aggressive subtype of endometrial cancer (EC)and a non-aggressive subtype of EC.
 19. The non-transitorycomputer-readable storage device of claim 18, wherein the thirdplurality of architectural features comprises architectural featuresfrom only the first plurality of architectural features.
 20. Thenon-transitory computer-readable storage device of claim 18, wherein theoperations further comprise: accessing a digitized H&E slide image of anAA patient of interest (POI), wherein the digitized H&E slide image ofthe AA POI indicates the AA POI has EC, and wherein the digitized H&Eslide image of the AA POI demonstrates tissue from a uterus of the AAPOI and at least a portion of a gynecologic tumor; defining a tumorregion in the digitized H&E slide image of the AA POI, wherein the tumorregion comprises a plurality of AA POI cells; defining a boundary foreach of the plurality of AA POI cells; classifying each of the pluralityof AA POI cells as one cell type of the plurality of cell types;generating one or more clusters of AA POI stromal TILs; extracting thethird plurality of architectural features from the one or more clustersof AA POI stromal TILs; generating a risk score for the AA POI based onthe third plurality of architectural features that were extracted fromthe one or more clusters of AA POI stromal TILs; providing the riskscore for the AA POI to the machine learning classifier; receiving, fromthe machine learning classifier, a classification of the EC of the AAPOI into either the aggressive subtype of EC or the non-aggressivesubtype of EC; and displaying the classification of the EC of the AAPOI.